The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 5 new columns ({'1', '2', '3', '4', '0'}) and 5 missing columns ({'chunk_id', 'file', 'source', 'page', 'text'}).
This happened while the json dataset builder was generating data using
hf://datasets/Cedric07/data/dataset.json (at revision e6a65076b57db5f980c9e36cd89579f89c31c8e0), [/tmp/hf-datasets-cache/medium/datasets/97618503472330-config-parquet-and-info-Cedric07-data-61401321/hub/datasets--Cedric07--data/snapshots/e6a65076b57db5f980c9e36cd89579f89c31c8e0/chunks_metadata.json (origin=hf://datasets/Cedric07/data@e6a65076b57db5f980c9e36cd89579f89c31c8e0/chunks_metadata.json), /tmp/hf-datasets-cache/medium/datasets/97618503472330-config-parquet-and-info-Cedric07-data-61401321/hub/datasets--Cedric07--data/snapshots/e6a65076b57db5f980c9e36cd89579f89c31c8e0/dataset.json (origin=hf://datasets/Cedric07/data@e6a65076b57db5f980c9e36cd89579f89c31c8e0/dataset.json)]
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1887, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 675, in write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
0: struct<file: string, page: int64, source: string, text: string>
child 0, file: string
child 1, page: int64
child 2, source: string
child 3, text: string
1: struct<page: int64, source: string, text: string>
child 0, page: int64
child 1, source: string
child 2, text: string
2: struct<page: int64, source: string, text: string>
child 0, page: int64
child 1, source: string
child 2, text: string
3: struct<page: int64, source: string, text: string>
child 0, page: int64
child 1, source: string
child 2, text: string
4: struct<page: int64, source: string, text: string>
child 0, page: int64
child 1, source: string
child 2, text: string
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 580
to
{'chunk_id': Value('string'), 'text': Value('string'), 'source': Value('string'), 'page': Value('int64'), 'file': Value('string')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 884, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 947, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1736, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1889, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 5 new columns ({'1', '2', '3', '4', '0'}) and 5 missing columns ({'chunk_id', 'file', 'source', 'page', 'text'}).
This happened while the json dataset builder was generating data using
hf://datasets/Cedric07/data/dataset.json (at revision e6a65076b57db5f980c9e36cd89579f89c31c8e0), [/tmp/hf-datasets-cache/medium/datasets/97618503472330-config-parquet-and-info-Cedric07-data-61401321/hub/datasets--Cedric07--data/snapshots/e6a65076b57db5f980c9e36cd89579f89c31c8e0/chunks_metadata.json (origin=hf://datasets/Cedric07/data@e6a65076b57db5f980c9e36cd89579f89c31c8e0/chunks_metadata.json), /tmp/hf-datasets-cache/medium/datasets/97618503472330-config-parquet-and-info-Cedric07-data-61401321/hub/datasets--Cedric07--data/snapshots/e6a65076b57db5f980c9e36cd89579f89c31c8e0/dataset.json (origin=hf://datasets/Cedric07/data@e6a65076b57db5f980c9e36cd89579f89c31c8e0/dataset.json)]
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
chunk_id
string | text
string | source
string | page
int64 | file
null |
|---|---|---|---|---|
resume.pdf_page1_12c3dbafff25
|
CHEUN DA
(438) 410-0783 | cedricdacheun@gmail.com |Montreal| LinkedIn | Website | Github
___________________________________________________________________________________________
Summary of Qualifications
● Experience in designing, testing, evaluating, and deploying machine learning model-based solutions
● Proficient in Python, Pandas, and Matplotlib for data exploration and visualization
● Experience with POWER BI and Tableau for creating interactive dashboards, providing real-time insights to
stakeholders
● Strong customer service skills, ensuring clear communication and quick problem resolution
● Excellent written and oral communication skills in both French and English
Technical Skills
Programming & Data:: Python, JavaScript (Node.js),Java
AI/ML Frameworks : PyTorch, TensorFlow, Hugging Face Transformers, OpenAI, LangChain,
LlamaIndex
Automation Tool: n8n, Cursor(IDE)
LLM Operations: Fine-tuning, RAG pipelines, prompt optimization, agent design
Data Engineering: Chroma, Pinecone, Weaviate, FAISS, PostgreSQL
Cloud Platforms: AWS (S3, EC2, SageMaker), Azure ML, GCP Vertex AI
APIs & Tools: FastAPI, Flask, Gradio, Streamlit, Docker, Git, RESTful APIs
NLP Techniques: Text classification, summarization, sentiment analysis, entity recognition,
embeddings
DevOps & MLOps: CI/CD, model monitoring, containerized deployments
Languages: French, English (bilingual)
Work Experience
Applied AI Researcher January 2022 to July 2024
UQTR Lab | Trois-Rivières (Quebec)
● Designed and deployed large-scale machine learning pipelines using Python, Scikit-Learn, TensorFlow, and Pandas,
achieving >99.9% model accuracy for network intrusion and fraud detection systems.
● Implemented data preprocessing and batch processing workflows, improving computation efficiency by 40%.
● Utilized GANs and Gaussian SMOTE to address class imbalance in large datasets.
● Optimized ML models with feature selection algorithms and meta-feature engineering, reducing features by 45%
while maintaining model precision.
● Built and deployed a RAG-based chatbot (Retriever-Augmented Generation) using LangChain, OpenAI, and LLAMA,
integrating real-time external data APIs.
● Developed a multi-agent monitoring system for RSS data streams, enhancing alert precision and latency by 60%.
● Deployed prototypes using Docker and Kubernetes on AWS for scalability and CI/CD integration.
Full Stack Developer February 2020 to August 2020
Travaris | Tunis, Tunisie
● Built high-performance web and mobile apps (ReactJS, React Native, Node.js) optimized for 200ms response time.
● Designed and deployed REST APIs (Feathers.js, GraphQL) with caching and SQL data storage, supporting 100k+
req/min at 99.9% uptime.
● Integrated automated web scraping and data ingestion pipelines with Selenium and SQL.
Web & Mobile Full Stack Developer Jun 2019 to August 2019
PERFORM VR │ France, Montpellier
Assisted the team in migrating an Android app to iOS using Swift and Xcode, optimizing performance to ensure a smooth
experience with response times under 100 ms on iPhone and iPad
● Assisted in cross-platform app migration (Android → iOS) using Swift and OpenGL, achieving <100ms latency.
● Implemented testing pipelines (unit, integration, regression), ensuring 95% test success rate.
|
resume.pdf
| 1
| null |
resume.pdf_page1_12c3dbafff25
|
CHEUN DA
(438) 410-0783 | cedricdacheun@gmail.com |Montreal| LinkedIn | Website | Github
___________________________________________________________________________________________
Summary of Qualifications
● Experience in designing, testing, evaluating, and deploying machine learning model-based solutions
● Proficient in Python, Pandas, and Matplotlib for data exploration and visualization
● Experience with POWER BI and Tableau for creating interactive dashboards, providing real-time insights to
stakeholders
● Strong customer service skills, ensuring clear communication and quick problem resolution
● Excellent written and oral communication skills in both French and English
Technical Skills
Programming & Data:: Python, JavaScript (Node.js),Java
AI/ML Frameworks : PyTorch, TensorFlow, Hugging Face Transformers, OpenAI, LangChain,
LlamaIndex
Automation Tool: n8n, Cursor(IDE)
LLM Operations: Fine-tuning, RAG pipelines, prompt optimization, agent design
Data Engineering: Chroma, Pinecone, Weaviate, FAISS, PostgreSQL
Cloud Platforms: AWS (S3, EC2, SageMaker), Azure ML, GCP Vertex AI
APIs & Tools: FastAPI, Flask, Gradio, Streamlit, Docker, Git, RESTful APIs
NLP Techniques: Text classification, summarization, sentiment analysis, entity recognition,
embeddings
DevOps & MLOps: CI/CD, model monitoring, containerized deployments
Languages: French, English (bilingual)
Work Experience
Applied AI Researcher January 2022 to July 2024
UQTR Lab | Trois-Rivières (Quebec)
● Designed and deployed large-scale machine learning pipelines using Python, Scikit-Learn, TensorFlow, and Pandas,
achieving >99.9% model accuracy for network intrusion and fraud detection systems.
● Implemented data preprocessing and batch processing workflows, improving computation efficiency by 40%.
● Utilized GANs and Gaussian SMOTE to address class imbalance in large datasets.
● Optimized ML models with feature selection algorithms and meta-feature engineering, reducing features by 45%
while maintaining model precision.
● Built and deployed a RAG-based chatbot (Retriever-Augmented Generation) using LangChain, OpenAI, and LLAMA,
integrating real-time external data APIs.
● Developed a multi-agent monitoring system for RSS data streams, enhancing alert precision and latency by 60%.
● Deployed prototypes using Docker and Kubernetes on AWS for scalability and CI/CD integration.
Full Stack Developer February 2020 to August 2020
Travaris | Tunis, Tunisie
● Built high-performance web and mobile apps (ReactJS, React Native, Node.js) optimized for 200ms response time.
● Designed and deployed REST APIs (Feathers.js, GraphQL) with caching and SQL data storage, supporting 100k+
req/min at 99.9% uptime.
● Integrated automated web scraping and data ingestion pipelines with Selenium and SQL.
Web & Mobile Full Stack Developer Jun 2019 to August 2019
PERFORM VR │ France, Montpellier
Assisted the team in migrating an Android app to iOS using Swift and Xcode, optimizing performance to ensure a smooth
experience with response times under 100 ms on iPhone and iPad
● Assisted in cross-platform app migration (Android → iOS) using Swift and OpenGL, achieving <100ms latency.
● Implemented testing pipelines (unit, integration, regression), ensuring 95% test success rate.
|
resume.pdf
| 1
| null |
resume.pdf_page1_12c3dbafff25
|
CHEUN DA
(438) 410-0783 | cedricdacheun@gmail.com |Montreal| LinkedIn | Website | Github
___________________________________________________________________________________________
Summary of Qualifications
● Experience in designing, testing, evaluating, and deploying machine learning model-based solutions
● Proficient in Python, Pandas, and Matplotlib for data exploration and visualization
● Experience with POWER BI and Tableau for creating interactive dashboards, providing real-time insights to
stakeholders
● Strong customer service skills, ensuring clear communication and quick problem resolution
● Excellent written and oral communication skills in both French and English
Technical Skills
Programming & Data:: Python, JavaScript (Node.js),Java
AI/ML Frameworks : PyTorch, TensorFlow, Hugging Face Transformers, OpenAI, LangChain,
LlamaIndex
Automation Tool: n8n, Cursor(IDE)
LLM Operations: Fine-tuning, RAG pipelines, prompt optimization, agent design
Data Engineering: Chroma, Pinecone, Weaviate, FAISS, PostgreSQL
Cloud Platforms: AWS (S3, EC2, SageMaker), Azure ML, GCP Vertex AI
APIs & Tools: FastAPI, Flask, Gradio, Streamlit, Docker, Git, RESTful APIs
NLP Techniques: Text classification, summarization, sentiment analysis, entity recognition,
embeddings
DevOps & MLOps: CI/CD, model monitoring, containerized deployments
Languages: French, English (bilingual)
Work Experience
Applied AI Researcher January 2022 to July 2024
UQTR Lab | Trois-Rivières (Quebec)
● Designed and deployed large-scale machine learning pipelines using Python, Scikit-Learn, TensorFlow, and Pandas,
achieving >99.9% model accuracy for network intrusion and fraud detection systems.
● Implemented data preprocessing and batch processing workflows, improving computation efficiency by 40%.
● Utilized GANs and Gaussian SMOTE to address class imbalance in large datasets.
● Optimized ML models with feature selection algorithms and meta-feature engineering, reducing features by 45%
while maintaining model precision.
● Built and deployed a RAG-based chatbot (Retriever-Augmented Generation) using LangChain, OpenAI, and LLAMA,
integrating real-time external data APIs.
● Developed a multi-agent monitoring system for RSS data streams, enhancing alert precision and latency by 60%.
● Deployed prototypes using Docker and Kubernetes on AWS for scalability and CI/CD integration.
Full Stack Developer February 2020 to August 2020
Travaris | Tunis, Tunisie
● Built high-performance web and mobile apps (ReactJS, React Native, Node.js) optimized for 200ms response time.
● Designed and deployed REST APIs (Feathers.js, GraphQL) with caching and SQL data storage, supporting 100k+
req/min at 99.9% uptime.
● Integrated automated web scraping and data ingestion pipelines with Selenium and SQL.
Web & Mobile Full Stack Developer Jun 2019 to August 2019
PERFORM VR │ France, Montpellier
Assisted the team in migrating an Android app to iOS using Swift and Xcode, optimizing performance to ensure a smooth
experience with response times under 100 ms on iPhone and iPad
● Assisted in cross-platform app migration (Android → iOS) using Swift and OpenGL, achieving <100ms latency.
● Implemented testing pipelines (unit, integration, regression), ensuring 95% test success rate.
|
resume.pdf
| 1
| null |
resume.pdf_page1_12c3dbafff25
|
CHEUN DA
(438) 410-0783 | cedricdacheun@gmail.com |Montreal| LinkedIn | Website | Github
___________________________________________________________________________________________
Summary of Qualifications
● Experience in designing, testing, evaluating, and deploying machine learning model-based solutions
● Proficient in Python, Pandas, and Matplotlib for data exploration and visualization
● Experience with POWER BI and Tableau for creating interactive dashboards, providing real-time insights to
stakeholders
● Strong customer service skills, ensuring clear communication and quick problem resolution
● Excellent written and oral communication skills in both French and English
Technical Skills
Programming & Data:: Python, JavaScript (Node.js),Java
AI/ML Frameworks : PyTorch, TensorFlow, Hugging Face Transformers, OpenAI, LangChain,
LlamaIndex
Automation Tool: n8n, Cursor(IDE)
LLM Operations: Fine-tuning, RAG pipelines, prompt optimization, agent design
Data Engineering: Chroma, Pinecone, Weaviate, FAISS, PostgreSQL
Cloud Platforms: AWS (S3, EC2, SageMaker), Azure ML, GCP Vertex AI
APIs & Tools: FastAPI, Flask, Gradio, Streamlit, Docker, Git, RESTful APIs
NLP Techniques: Text classification, summarization, sentiment analysis, entity recognition,
embeddings
DevOps & MLOps: CI/CD, model monitoring, containerized deployments
Languages: French, English (bilingual)
Work Experience
Applied AI Researcher January 2022 to July 2024
UQTR Lab | Trois-Rivières (Quebec)
● Designed and deployed large-scale machine learning pipelines using Python, Scikit-Learn, TensorFlow, and Pandas,
achieving >99.9% model accuracy for network intrusion and fraud detection systems.
● Implemented data preprocessing and batch processing workflows, improving computation efficiency by 40%.
● Utilized GANs and Gaussian SMOTE to address class imbalance in large datasets.
● Optimized ML models with feature selection algorithms and meta-feature engineering, reducing features by 45%
while maintaining model precision.
● Built and deployed a RAG-based chatbot (Retriever-Augmented Generation) using LangChain, OpenAI, and LLAMA,
integrating real-time external data APIs.
● Developed a multi-agent monitoring system for RSS data streams, enhancing alert precision and latency by 60%.
● Deployed prototypes using Docker and Kubernetes on AWS for scalability and CI/CD integration.
Full Stack Developer February 2020 to August 2020
Travaris | Tunis, Tunisie
● Built high-performance web and mobile apps (ReactJS, React Native, Node.js) optimized for 200ms response time.
● Designed and deployed REST APIs (Feathers.js, GraphQL) with caching and SQL data storage, supporting 100k+
req/min at 99.9% uptime.
● Integrated automated web scraping and data ingestion pipelines with Selenium and SQL.
Web & Mobile Full Stack Developer Jun 2019 to August 2019
PERFORM VR │ France, Montpellier
Assisted the team in migrating an Android app to iOS using Swift and Xcode, optimizing performance to ensure a smooth
experience with response times under 100 ms on iPhone and iPad
● Assisted in cross-platform app migration (Android → iOS) using Swift and OpenGL, achieving <100ms latency.
● Implemented testing pipelines (unit, integration, regression), ensuring 95% test success rate.
|
resume.pdf
| 1
| null |
resume.pdf_page1_12c3dbafff25
|
CHEUN DA
(438) 410-0783 | cedricdacheun@gmail.com |Montreal| LinkedIn | Website | Github
___________________________________________________________________________________________
Summary of Qualifications
● Experience in designing, testing, evaluating, and deploying machine learning model-based solutions
● Proficient in Python, Pandas, and Matplotlib for data exploration and visualization
● Experience with POWER BI and Tableau for creating interactive dashboards, providing real-time insights to
stakeholders
● Strong customer service skills, ensuring clear communication and quick problem resolution
● Excellent written and oral communication skills in both French and English
Technical Skills
Programming & Data:: Python, JavaScript (Node.js),Java
AI/ML Frameworks : PyTorch, TensorFlow, Hugging Face Transformers, OpenAI, LangChain,
LlamaIndex
Automation Tool: n8n, Cursor(IDE)
LLM Operations: Fine-tuning, RAG pipelines, prompt optimization, agent design
Data Engineering: Chroma, Pinecone, Weaviate, FAISS, PostgreSQL
Cloud Platforms: AWS (S3, EC2, SageMaker), Azure ML, GCP Vertex AI
APIs & Tools: FastAPI, Flask, Gradio, Streamlit, Docker, Git, RESTful APIs
NLP Techniques: Text classification, summarization, sentiment analysis, entity recognition,
embeddings
DevOps & MLOps: CI/CD, model monitoring, containerized deployments
Languages: French, English (bilingual)
Work Experience
Applied AI Researcher January 2022 to July 2024
UQTR Lab | Trois-Rivières (Quebec)
● Designed and deployed large-scale machine learning pipelines using Python, Scikit-Learn, TensorFlow, and Pandas,
achieving >99.9% model accuracy for network intrusion and fraud detection systems.
● Implemented data preprocessing and batch processing workflows, improving computation efficiency by 40%.
● Utilized GANs and Gaussian SMOTE to address class imbalance in large datasets.
● Optimized ML models with feature selection algorithms and meta-feature engineering, reducing features by 45%
while maintaining model precision.
● Built and deployed a RAG-based chatbot (Retriever-Augmented Generation) using LangChain, OpenAI, and LLAMA,
integrating real-time external data APIs.
● Developed a multi-agent monitoring system for RSS data streams, enhancing alert precision and latency by 60%.
● Deployed prototypes using Docker and Kubernetes on AWS for scalability and CI/CD integration.
Full Stack Developer February 2020 to August 2020
Travaris | Tunis, Tunisie
● Built high-performance web and mobile apps (ReactJS, React Native, Node.js) optimized for 200ms response time.
● Designed and deployed REST APIs (Feathers.js, GraphQL) with caching and SQL data storage, supporting 100k+
req/min at 99.9% uptime.
● Integrated automated web scraping and data ingestion pipelines with Selenium and SQL.
Web & Mobile Full Stack Developer Jun 2019 to August 2019
PERFORM VR │ France, Montpellier
Assisted the team in migrating an Android app to iOS using Swift and Xcode, optimizing performance to ensure a smooth
experience with response times under 100 ms on iPhone and iPad
● Assisted in cross-platform app migration (Android → iOS) using Swift and OpenGL, achieving <100ms latency.
● Implemented testing pipelines (unit, integration, regression), ensuring 95% test success rate.
|
resume.pdf
| 1
| null |
resume.pdf_page1_12c3dbafff25
|
CHEUN DA
(438) 410-0783 | cedricdacheun@gmail.com |Montreal| LinkedIn | Website | Github
___________________________________________________________________________________________
Summary of Qualifications
● Experience in designing, testing, evaluating, and deploying machine learning model-based solutions
● Proficient in Python, Pandas, and Matplotlib for data exploration and visualization
● Experience with POWER BI and Tableau for creating interactive dashboards, providing real-time insights to
stakeholders
● Strong customer service skills, ensuring clear communication and quick problem resolution
● Excellent written and oral communication skills in both French and English
Technical Skills
Programming & Data:: Python, JavaScript (Node.js),Java
AI/ML Frameworks : PyTorch, TensorFlow, Hugging Face Transformers, OpenAI, LangChain,
LlamaIndex
Automation Tool: n8n, Cursor(IDE)
LLM Operations: Fine-tuning, RAG pipelines, prompt optimization, agent design
Data Engineering: Chroma, Pinecone, Weaviate, FAISS, PostgreSQL
Cloud Platforms: AWS (S3, EC2, SageMaker), Azure ML, GCP Vertex AI
APIs & Tools: FastAPI, Flask, Gradio, Streamlit, Docker, Git, RESTful APIs
NLP Techniques: Text classification, summarization, sentiment analysis, entity recognition,
embeddings
DevOps & MLOps: CI/CD, model monitoring, containerized deployments
Languages: French, English (bilingual)
Work Experience
Applied AI Researcher January 2022 to July 2024
UQTR Lab | Trois-Rivières (Quebec)
● Designed and deployed large-scale machine learning pipelines using Python, Scikit-Learn, TensorFlow, and Pandas,
achieving >99.9% model accuracy for network intrusion and fraud detection systems.
● Implemented data preprocessing and batch processing workflows, improving computation efficiency by 40%.
● Utilized GANs and Gaussian SMOTE to address class imbalance in large datasets.
● Optimized ML models with feature selection algorithms and meta-feature engineering, reducing features by 45%
while maintaining model precision.
● Built and deployed a RAG-based chatbot (Retriever-Augmented Generation) using LangChain, OpenAI, and LLAMA,
integrating real-time external data APIs.
● Developed a multi-agent monitoring system for RSS data streams, enhancing alert precision and latency by 60%.
● Deployed prototypes using Docker and Kubernetes on AWS for scalability and CI/CD integration.
Full Stack Developer February 2020 to August 2020
Travaris | Tunis, Tunisie
● Built high-performance web and mobile apps (ReactJS, React Native, Node.js) optimized for 200ms response time.
● Designed and deployed REST APIs (Feathers.js, GraphQL) with caching and SQL data storage, supporting 100k+
req/min at 99.9% uptime.
● Integrated automated web scraping and data ingestion pipelines with Selenium and SQL.
Web & Mobile Full Stack Developer Jun 2019 to August 2019
PERFORM VR │ France, Montpellier
Assisted the team in migrating an Android app to iOS using Swift and Xcode, optimizing performance to ensure a smooth
experience with response times under 100 ms on iPhone and iPad
● Assisted in cross-platform app migration (Android → iOS) using Swift and OpenGL, achieving <100ms latency.
● Implemented testing pipelines (unit, integration, regression), ensuring 95% test success rate.
|
resume.pdf
| 1
| null |
resume.pdf_page2_0d76bfe477ef
|
● Managed version control on GitLab, ensuring smooth versioning with a continuous delivery cycle.
Education & Certifications
Master's in Applied Mathematics and Computer Science Janvier 2022 a Juillet 2024
Université du Québec à Trois-Rivières (UQTR) │ Trois-Rivières, Quebec
Bachelor's Degree in Computer Engineering January 2015 to December 2020
ESPRIT École Supérieure Privée d'Ingénierie et de Technologies│ Tunis, Tunisia
Data Analyst Program – NPower Canada August 2025 to October 2025
IBM Certified Data Analyst Professional October 2025
Microsoft Azure AI Certification September 2025
AWS Solution Architect Professional Certification February 2025
AWS Cloud Certification November 2024
Volunteer Experience
Volunteer at Afromusée September 2024 to December 2024
Afromusée │ Montreal, QC
● Contributed to the planning and organization of cultural events and conferences, ensuring smooth execution and a
20% increase in attendance compared to previous editions
● Welcomed participants at events and conferences, providing a warm and professional experience, which resulted in
a 95% satisfaction rate in post-event evaluations
● Actively participated in networking nights every Sunday, facilitating exchanges and collaborations between
participants, contributing to new partnerships and cultural projects. These events helped strengthen community
bonds and increased engagement by 30%
● Assisted during museum exhibits, providing enriching information to visitors, contributing to an enhanced
educational experience and increasing visitor retention rates
|
resume.pdf
| 2
| null |
resume.pdf_page2_0d76bfe477ef
|
● Managed version control on GitLab, ensuring smooth versioning with a continuous delivery cycle.
Education & Certifications
Master's in Applied Mathematics and Computer Science Janvier 2022 a Juillet 2024
Université du Québec à Trois-Rivières (UQTR) │ Trois-Rivières, Quebec
Bachelor's Degree in Computer Engineering January 2015 to December 2020
ESPRIT École Supérieure Privée d'Ingénierie et de Technologies│ Tunis, Tunisia
Data Analyst Program – NPower Canada August 2025 to October 2025
IBM Certified Data Analyst Professional October 2025
Microsoft Azure AI Certification September 2025
AWS Solution Architect Professional Certification February 2025
AWS Cloud Certification November 2024
Volunteer Experience
Volunteer at Afromusée September 2024 to December 2024
Afromusée │ Montreal, QC
● Contributed to the planning and organization of cultural events and conferences, ensuring smooth execution and a
20% increase in attendance compared to previous editions
● Welcomed participants at events and conferences, providing a warm and professional experience, which resulted in
a 95% satisfaction rate in post-event evaluations
● Actively participated in networking nights every Sunday, facilitating exchanges and collaborations between
participants, contributing to new partnerships and cultural projects. These events helped strengthen community
bonds and increased engagement by 30%
● Assisted during museum exhibits, providing enriching information to visitors, contributing to an enhanced
educational experience and increasing visitor retention rates
|
resume.pdf
| 2
| null |
resume.pdf_page2_0d76bfe477ef
|
● Managed version control on GitLab, ensuring smooth versioning with a continuous delivery cycle.
Education & Certifications
Master's in Applied Mathematics and Computer Science Janvier 2022 a Juillet 2024
Université du Québec à Trois-Rivières (UQTR) │ Trois-Rivières, Quebec
Bachelor's Degree in Computer Engineering January 2015 to December 2020
ESPRIT École Supérieure Privée d'Ingénierie et de Technologies│ Tunis, Tunisia
Data Analyst Program – NPower Canada August 2025 to October 2025
IBM Certified Data Analyst Professional October 2025
Microsoft Azure AI Certification September 2025
AWS Solution Architect Professional Certification February 2025
AWS Cloud Certification November 2024
Volunteer Experience
Volunteer at Afromusée September 2024 to December 2024
Afromusée │ Montreal, QC
● Contributed to the planning and organization of cultural events and conferences, ensuring smooth execution and a
20% increase in attendance compared to previous editions
● Welcomed participants at events and conferences, providing a warm and professional experience, which resulted in
a 95% satisfaction rate in post-event evaluations
● Actively participated in networking nights every Sunday, facilitating exchanges and collaborations between
participants, contributing to new partnerships and cultural projects. These events helped strengthen community
bonds and increased engagement by 30%
● Assisted during museum exhibits, providing enriching information to visitors, contributing to an enhanced
educational experience and increasing visitor retention rates
|
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|
Contact
cedricdacheun@gmail.com
www.linkedin.com/in/cheun-da
(LinkedIn)
cheunanthony.github.io/ (Portfolio)
Top Skills
Web Scraping
Databases
Data Manipulation
Languages
French (Native or Bilingual)
Français (Native or Bilingual)
Anglais (Professional Working)
English (Full Professional)
Certifications
AWS Certified Solutions Architect –
Associate
Swift Programming
Azure Databricks & Spark For Data
Engineers
Python Datascience Toolbox
Cloud training
Cheun DA
Data Analyst|Data Scientist/Machine Learning Engineer|Certified
AWS Solution Architect Associate|Driving Business Value Through
Insights, Scalable Data Systems & AI
Montreal, Quebec, Canada
Summary
I’ve always believed that data tells a story, and my passion lies in
uncovering those stories to drive smarter decisions.
– I enjoy exploring datasets, identifying trends, and transforming raw
numbers into clear, actionable insights that support smarter business
decisions.
- I’m driven by the challenge of building reliable data pipelines,
optimizing storage, and ensuring data is accessible, scalable, and
high quality.
– I am excited by predictive modeling, AI, and advanced algorithms
that reveal hidden patterns and enable automation, personalization,
and innovation.
- I see every challenge as an opportunity to build smarter, more
efficient data ecosystems that power innovation.
Experience
Université du Québec à Trois-Rivières
Applied researcher in AI
January 2022 - July 2024 (2 years 7 months)
Trois-Rivières, Quebec, Canada
Applied AI Researcher with a strong focus on Anomaly Detection, Natural
Language Processing (NLP), and a keen interest in Computer Vision and
Finance applications of Machine Learning. Over 2 years of experience
in designing, implementing, and deploying machine learning models to
solve real-world problems such as intrusion detection, fraud detection, and
drowsiness detection.
Page 1 of 5
|
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| 1
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linked_page.pdf_page1_20e1d5dd71e6
|
Contact
cedricdacheun@gmail.com
www.linkedin.com/in/cheun-da
(LinkedIn)
cheunanthony.github.io/ (Portfolio)
Top Skills
Web Scraping
Databases
Data Manipulation
Languages
French (Native or Bilingual)
Français (Native or Bilingual)
Anglais (Professional Working)
English (Full Professional)
Certifications
AWS Certified Solutions Architect –
Associate
Swift Programming
Azure Databricks & Spark For Data
Engineers
Python Datascience Toolbox
Cloud training
Cheun DA
Data Analyst|Data Scientist/Machine Learning Engineer|Certified
AWS Solution Architect Associate|Driving Business Value Through
Insights, Scalable Data Systems & AI
Montreal, Quebec, Canada
Summary
I’ve always believed that data tells a story, and my passion lies in
uncovering those stories to drive smarter decisions.
– I enjoy exploring datasets, identifying trends, and transforming raw
numbers into clear, actionable insights that support smarter business
decisions.
- I’m driven by the challenge of building reliable data pipelines,
optimizing storage, and ensuring data is accessible, scalable, and
high quality.
– I am excited by predictive modeling, AI, and advanced algorithms
that reveal hidden patterns and enable automation, personalization,
and innovation.
- I see every challenge as an opportunity to build smarter, more
efficient data ecosystems that power innovation.
Experience
Université du Québec à Trois-Rivières
Applied researcher in AI
January 2022 - July 2024 (2 years 7 months)
Trois-Rivières, Quebec, Canada
Applied AI Researcher with a strong focus on Anomaly Detection, Natural
Language Processing (NLP), and a keen interest in Computer Vision and
Finance applications of Machine Learning. Over 2 years of experience
in designing, implementing, and deploying machine learning models to
solve real-world problems such as intrusion detection, fraud detection, and
drowsiness detection.
Page 1 of 5
|
linked_page.pdf
| 1
| null |
linked_page.pdf_page1_20e1d5dd71e6
|
Contact
cedricdacheun@gmail.com
www.linkedin.com/in/cheun-da
(LinkedIn)
cheunanthony.github.io/ (Portfolio)
Top Skills
Web Scraping
Databases
Data Manipulation
Languages
French (Native or Bilingual)
Français (Native or Bilingual)
Anglais (Professional Working)
English (Full Professional)
Certifications
AWS Certified Solutions Architect –
Associate
Swift Programming
Azure Databricks & Spark For Data
Engineers
Python Datascience Toolbox
Cloud training
Cheun DA
Data Analyst|Data Scientist/Machine Learning Engineer|Certified
AWS Solution Architect Associate|Driving Business Value Through
Insights, Scalable Data Systems & AI
Montreal, Quebec, Canada
Summary
I’ve always believed that data tells a story, and my passion lies in
uncovering those stories to drive smarter decisions.
– I enjoy exploring datasets, identifying trends, and transforming raw
numbers into clear, actionable insights that support smarter business
decisions.
- I’m driven by the challenge of building reliable data pipelines,
optimizing storage, and ensuring data is accessible, scalable, and
high quality.
– I am excited by predictive modeling, AI, and advanced algorithms
that reveal hidden patterns and enable automation, personalization,
and innovation.
- I see every challenge as an opportunity to build smarter, more
efficient data ecosystems that power innovation.
Experience
Université du Québec à Trois-Rivières
Applied researcher in AI
January 2022 - July 2024 (2 years 7 months)
Trois-Rivières, Quebec, Canada
Applied AI Researcher with a strong focus on Anomaly Detection, Natural
Language Processing (NLP), and a keen interest in Computer Vision and
Finance applications of Machine Learning. Over 2 years of experience
in designing, implementing, and deploying machine learning models to
solve real-world problems such as intrusion detection, fraud detection, and
drowsiness detection.
Page 1 of 5
|
linked_page.pdf
| 1
| null |
linked_page.pdf_page2_efbc83e2bf71
|
Developed two advanced frameworks for intrusion detection in networks and
credit card fraud detection, using
SKlearn, Numpy, Pandas, Matplotlib, Seaborn, and TensorFlow, achieving
model accuracies above 99.96%
● Solved data imbalance using techniques like GAN, Gaussian SMOTE to
improve machine learning model performance
● Optimized machine learning models by adding meta-features, applying
selection techniques such as particle swarm
optimization, reducing overfitting, and reducing computational cost with feature
reduction from 87 to 47
● Created multi-agent LLMs to monitor RSS feeds for detecting promotional
offers, with the ability to send detailed
notifications about products found. Additionally, developed an insurance
domain-specific chatbot based on the RAG
(Retriever-Augmented Generation) model, using Python, LangChain, OpenAI,
LLAMA, and Claude. This chatbot
provides accurate and personalized responses based on real-time external
data
● Developed a real-time drowsiness detection system using TensorFlow,
YOLO, and OpenCV
SERF Burkina
IT consultant at SERF
August 2021 - December 2021 (5 months)
Ouagadougou, Burkina Faso
The project involved the creation of a dynamic web platform aimed at
enhancing the visibility of SERF Company and facilitating its transition into the
digital realm. The platform is designed to provide seamless user experience,
improve operational efficiency, and streamline job offer management.
Key Responsibilities:
As an IT Consultant for this project, my role encompassed both technical and
strategic tasks aimed at ensuring the successful development and deployment
of the web platform. My key contributions included:
RESTful Web Service Development:
Developed and integrated RESTful APIs to enable seamless communication
between the front-end and back-end components of the platform. This ensured
scalability, performance, and the ability to support future integrations.
Administrator Interface Development:
Page 2 of 5
|
linked_page.pdf
| 2
| null |
linked_page.pdf_page2_efbc83e2bf71
|
Developed two advanced frameworks for intrusion detection in networks and
credit card fraud detection, using
SKlearn, Numpy, Pandas, Matplotlib, Seaborn, and TensorFlow, achieving
model accuracies above 99.96%
● Solved data imbalance using techniques like GAN, Gaussian SMOTE to
improve machine learning model performance
● Optimized machine learning models by adding meta-features, applying
selection techniques such as particle swarm
optimization, reducing overfitting, and reducing computational cost with feature
reduction from 87 to 47
● Created multi-agent LLMs to monitor RSS feeds for detecting promotional
offers, with the ability to send detailed
notifications about products found. Additionally, developed an insurance
domain-specific chatbot based on the RAG
(Retriever-Augmented Generation) model, using Python, LangChain, OpenAI,
LLAMA, and Claude. This chatbot
provides accurate and personalized responses based on real-time external
data
● Developed a real-time drowsiness detection system using TensorFlow,
YOLO, and OpenCV
SERF Burkina
IT consultant at SERF
August 2021 - December 2021 (5 months)
Ouagadougou, Burkina Faso
The project involved the creation of a dynamic web platform aimed at
enhancing the visibility of SERF Company and facilitating its transition into the
digital realm. The platform is designed to provide seamless user experience,
improve operational efficiency, and streamline job offer management.
Key Responsibilities:
As an IT Consultant for this project, my role encompassed both technical and
strategic tasks aimed at ensuring the successful development and deployment
of the web platform. My key contributions included:
RESTful Web Service Development:
Developed and integrated RESTful APIs to enable seamless communication
between the front-end and back-end components of the platform. This ensured
scalability, performance, and the ability to support future integrations.
Administrator Interface Development:
Page 2 of 5
|
linked_page.pdf
| 2
| null |
linked_page.pdf_page2_efbc83e2bf71
|
Developed two advanced frameworks for intrusion detection in networks and
credit card fraud detection, using
SKlearn, Numpy, Pandas, Matplotlib, Seaborn, and TensorFlow, achieving
model accuracies above 99.96%
● Solved data imbalance using techniques like GAN, Gaussian SMOTE to
improve machine learning model performance
● Optimized machine learning models by adding meta-features, applying
selection techniques such as particle swarm
optimization, reducing overfitting, and reducing computational cost with feature
reduction from 87 to 47
● Created multi-agent LLMs to monitor RSS feeds for detecting promotional
offers, with the ability to send detailed
notifications about products found. Additionally, developed an insurance
domain-specific chatbot based on the RAG
(Retriever-Augmented Generation) model, using Python, LangChain, OpenAI,
LLAMA, and Claude. This chatbot
provides accurate and personalized responses based on real-time external
data
● Developed a real-time drowsiness detection system using TensorFlow,
YOLO, and OpenCV
SERF Burkina
IT consultant at SERF
August 2021 - December 2021 (5 months)
Ouagadougou, Burkina Faso
The project involved the creation of a dynamic web platform aimed at
enhancing the visibility of SERF Company and facilitating its transition into the
digital realm. The platform is designed to provide seamless user experience,
improve operational efficiency, and streamline job offer management.
Key Responsibilities:
As an IT Consultant for this project, my role encompassed both technical and
strategic tasks aimed at ensuring the successful development and deployment
of the web platform. My key contributions included:
RESTful Web Service Development:
Developed and integrated RESTful APIs to enable seamless communication
between the front-end and back-end components of the platform. This ensured
scalability, performance, and the ability to support future integrations.
Administrator Interface Development:
Page 2 of 5
|
linked_page.pdf
| 2
| null |
linked_page.pdf_page3_ebe657ea374f
|
Designed and implemented an intuitive administrator interface, empowering
internal teams to efficiently manage platform content, monitor user activities,
and oversee job offer postings. This interface featured robust access control
and streamlined workflows.
Job Offer Management System:
Developed a comprehensive job offer management system, allowing users to
easily post, edit, and track job offers. This system was integrated with backend
databases to ensure real-time updates and accurate tracking.
Multilingual Platform Implementation:
Led the translation and localization of the platform into French and English,
ensuring accessibility and a seamless experience for users across different
linguistic backgrounds. Implemented internationalization (i18n) best practices
to ensure scalability for future languages.
Deployment & Security:
Oversaw the deployment of the platform on a secure VPS (Virtual Private
Server), ensuring that the system was optimized for performance and fully
secured against potential vulnerabilities. Managed server configuration,
database integration, and platform stability to ensure a smooth launch.
Travaris
Full stack React js and React native developper
February 2020 - August 2020 (7 months)
Tunisia
Developed and implemented a high-performance web and mobile app with
ReactJS and React Native, allowing users (tourists and travelers) to view
detailed information about places (hotels, parks, museums, etc.) in a country
via an interactive map based on OpenStreetMap. The app achieved an
average response time of under 200 ms for loading place information.
Integrated Selenium for web scraping, enabling automated collection of data
on over 10,000 tourist destinations. The extracted data was pre-processed and
stored efficiently in an SQL database, with a 95% real-time data update rate.
Developed an optimized REST API with Feathers.js, combined with GraphQL
and an intelligent caching system, reducing query response times by 50%
compared to a traditional architecture. The API can handle up to 100,000
requests per minute with 99.9% uptime.
Page 3 of 5
|
linked_page.pdf
| 3
| null |
linked_page.pdf_page3_ebe657ea374f
|
Designed and implemented an intuitive administrator interface, empowering
internal teams to efficiently manage platform content, monitor user activities,
and oversee job offer postings. This interface featured robust access control
and streamlined workflows.
Job Offer Management System:
Developed a comprehensive job offer management system, allowing users to
easily post, edit, and track job offers. This system was integrated with backend
databases to ensure real-time updates and accurate tracking.
Multilingual Platform Implementation:
Led the translation and localization of the platform into French and English,
ensuring accessibility and a seamless experience for users across different
linguistic backgrounds. Implemented internationalization (i18n) best practices
to ensure scalability for future languages.
Deployment & Security:
Oversaw the deployment of the platform on a secure VPS (Virtual Private
Server), ensuring that the system was optimized for performance and fully
secured against potential vulnerabilities. Managed server configuration,
database integration, and platform stability to ensure a smooth launch.
Travaris
Full stack React js and React native developper
February 2020 - August 2020 (7 months)
Tunisia
Developed and implemented a high-performance web and mobile app with
ReactJS and React Native, allowing users (tourists and travelers) to view
detailed information about places (hotels, parks, museums, etc.) in a country
via an interactive map based on OpenStreetMap. The app achieved an
average response time of under 200 ms for loading place information.
Integrated Selenium for web scraping, enabling automated collection of data
on over 10,000 tourist destinations. The extracted data was pre-processed and
stored efficiently in an SQL database, with a 95% real-time data update rate.
Developed an optimized REST API with Feathers.js, combined with GraphQL
and an intelligent caching system, reducing query response times by 50%
compared to a traditional architecture. The API can handle up to 100,000
requests per minute with 99.9% uptime.
Page 3 of 5
|
linked_page.pdf
| 3
| null |
linked_page.pdf_page3_ebe657ea374f
|
Designed and implemented an intuitive administrator interface, empowering
internal teams to efficiently manage platform content, monitor user activities,
and oversee job offer postings. This interface featured robust access control
and streamlined workflows.
Job Offer Management System:
Developed a comprehensive job offer management system, allowing users to
easily post, edit, and track job offers. This system was integrated with backend
databases to ensure real-time updates and accurate tracking.
Multilingual Platform Implementation:
Led the translation and localization of the platform into French and English,
ensuring accessibility and a seamless experience for users across different
linguistic backgrounds. Implemented internationalization (i18n) best practices
to ensure scalability for future languages.
Deployment & Security:
Oversaw the deployment of the platform on a secure VPS (Virtual Private
Server), ensuring that the system was optimized for performance and fully
secured against potential vulnerabilities. Managed server configuration,
database integration, and platform stability to ensure a smooth launch.
Travaris
Full stack React js and React native developper
February 2020 - August 2020 (7 months)
Tunisia
Developed and implemented a high-performance web and mobile app with
ReactJS and React Native, allowing users (tourists and travelers) to view
detailed information about places (hotels, parks, museums, etc.) in a country
via an interactive map based on OpenStreetMap. The app achieved an
average response time of under 200 ms for loading place information.
Integrated Selenium for web scraping, enabling automated collection of data
on over 10,000 tourist destinations. The extracted data was pre-processed and
stored efficiently in an SQL database, with a 95% real-time data update rate.
Developed an optimized REST API with Feathers.js, combined with GraphQL
and an intelligent caching system, reducing query response times by 50%
compared to a traditional architecture. The API can handle up to 100,000
requests per minute with 99.9% uptime.
Page 3 of 5
|
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| null |
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|
PERFORM VR
Full Stack iOS Developer
June 2019 - August 2019 (3 months)
Région de Montpellier, France
Assisted the team in migrating an Android app to iOS using Swift and Xcode,
optimizing performance to ensure a smooth experience with response times
under 100 ms on iPhone and iPad.
Designed the app using Adobe XD, integrating OpenGL and Google VR for an
immersive virtual reality experience, allowing users to burn up to 500 calories
per 30-minute session, maintaining 60 fps rendering fluidity.
Implemented unit, integration, and regression tests to ensure maximum
stability with a test success rate of over 95% across all platforms. Continuously
debugged to optimize performance and ensure memory usage under 30 MB
per session.
Managed version control on GitLab, ensuring smooth versioning with a
continuous delivery cycle. Created two types of releases: a demo version
optimized for a quick trial experience and a full version offering advanced
features, with updates every two weeks to improve user experience.
Xtensus
Full Stack JEE & Symfony Developer
June 2017 - July 2018 (1 year 2 months)
Governorate of Tunis, Tunisia
-Implemented microservices architecture for better scalability and
maintainability of the platform
-Built automated CI/CD pipelines with GitLab CI to ensure continuous
deployment of the application to AWS EC2 instances
-Utilized Docker to containerize the application, enabling consistent
environments across development, testing, and production
-Developed and optimized REST APIs using Caching and GraphQL for
managing orders, users, payments, and inventory
-Front-End Development (Symfony-based)
-Back-End Development (JEE/Spring & Symfony)
Education
Page 4 of 5
|
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| 4
| null |
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|
PERFORM VR
Full Stack iOS Developer
June 2019 - August 2019 (3 months)
Région de Montpellier, France
Assisted the team in migrating an Android app to iOS using Swift and Xcode,
optimizing performance to ensure a smooth experience with response times
under 100 ms on iPhone and iPad.
Designed the app using Adobe XD, integrating OpenGL and Google VR for an
immersive virtual reality experience, allowing users to burn up to 500 calories
per 30-minute session, maintaining 60 fps rendering fluidity.
Implemented unit, integration, and regression tests to ensure maximum
stability with a test success rate of over 95% across all platforms. Continuously
debugged to optimize performance and ensure memory usage under 30 MB
per session.
Managed version control on GitLab, ensuring smooth versioning with a
continuous delivery cycle. Created two types of releases: a demo version
optimized for a quick trial experience and a full version offering advanced
features, with updates every two weeks to improve user experience.
Xtensus
Full Stack JEE & Symfony Developer
June 2017 - July 2018 (1 year 2 months)
Governorate of Tunis, Tunisia
-Implemented microservices architecture for better scalability and
maintainability of the platform
-Built automated CI/CD pipelines with GitLab CI to ensure continuous
deployment of the application to AWS EC2 instances
-Utilized Docker to containerize the application, enabling consistent
environments across development, testing, and production
-Developed and optimized REST APIs using Caching and GraphQL for
managing orders, users, payments, and inventory
-Front-End Development (Symfony-based)
-Back-End Development (JEE/Spring & Symfony)
Education
Page 4 of 5
|
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| 4
| null |
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|
PERFORM VR
Full Stack iOS Developer
June 2019 - August 2019 (3 months)
Région de Montpellier, France
Assisted the team in migrating an Android app to iOS using Swift and Xcode,
optimizing performance to ensure a smooth experience with response times
under 100 ms on iPhone and iPad.
Designed the app using Adobe XD, integrating OpenGL and Google VR for an
immersive virtual reality experience, allowing users to burn up to 500 calories
per 30-minute session, maintaining 60 fps rendering fluidity.
Implemented unit, integration, and regression tests to ensure maximum
stability with a test success rate of over 95% across all platforms. Continuously
debugged to optimize performance and ensure memory usage under 30 MB
per session.
Managed version control on GitLab, ensuring smooth versioning with a
continuous delivery cycle. Created two types of releases: a demo version
optimized for a quick trial experience and a full version offering advanced
features, with updates every two weeks to improve user experience.
Xtensus
Full Stack JEE & Symfony Developer
June 2017 - July 2018 (1 year 2 months)
Governorate of Tunis, Tunisia
-Implemented microservices architecture for better scalability and
maintainability of the platform
-Built automated CI/CD pipelines with GitLab CI to ensure continuous
deployment of the application to AWS EC2 instances
-Utilized Docker to containerize the application, enabling consistent
environments across development, testing, and production
-Developed and optimized REST APIs using Caching and GraphQL for
managing orders, users, payments, and inventory
-Front-End Development (Symfony-based)
-Back-End Development (JEE/Spring & Symfony)
Education
Page 4 of 5
|
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| 4
| null |
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|
NPower
Data analyst certification · (August 2025 - November 2025)
Université du Québec à Trois-Rivières
Master's degree in Applied Mathematics and Computer Science · (January
2022 - December 2024)
Ecole Supérieure Privée d'Ingénierie et de Technologies - ESPRIT
Bac+4, Engineering computer Science · (2015 - 2020)
ESPRIT
Informatique
Page 5 of 5
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github_readmes_portfolio.md_1c3837cb49d6
|
# Portfolio Projects
## KnowBot
- Description: Knowbot is designed to be an intelligent assistant that can seamlessly interact with your organization’s internal knowledge base. Whether it's through voice or text queries, this assistant taps into your existing documents, policies, research papers, and other content to provide accurate, up-to-date, and contextually relevant answers. It blends retrieval-augmented generation (RAG) for data sourcing and a large language model (LLM) for sophisticated reasoning and answer generation.
- Tech: Python, Openai, Gradio, Whisper, LLM
## Multi-Agent Financial Advisor System
- Description: This project is a production-ready multi-agent AI financial advisory system designed to deliver holistic, personalized financial guidance.
It integrates investment advisory, tax optimization, and retirement planning into a unified intelligent platform.
The system leverages:
Specialized AI agents coordinated by an Orchestrator Agent
Real-time market intelligence
Advanced Retrieval-Augmented Generation (RAG) with hybrid search
Structured financial data pipelines
Educational visual generation using OpenAI Images
The advisor interprets a user’s full financial profile (income, assets, liabilities, goals, risk tolerance) and produces coherent, actionable financial plans while resolving conflicts between investment growth, tax efficiency, and long-term retirement objectives.
- Tech: Python, Gradio, Langchain, Vector-Database, Semantic Search, RAG
## AzureQbot
- Description: A modern React frontend for a Knowledge Base Chatbot hosted on Azure. Features a sleek chat interface with Markdown rendering, avatars, message timestamps, dark mode, and seamless integration with a Python backend API.
- Tech: React, FastAPI, Azure, QA, Knowledgebase
## AI Brochure Generator
- Description: In this project, we built a highly efficient brouchure generator using LLM. We also added the option of translating the generated brochure. The solution process can be divided into three main stages. To build our graphical interface we used gradio wich is an open-source Python framework that simplifies the creation of interactive web interfaces for machine learning models, APIs, or any Python function
The first step is to build a website scrapper that can retrieve the content of a given url website.
After scrapping the website, we'll send the useful website content to an LLM model. The LLM model will generate a brochure by summarizing and extracting the useful information. We choose Chatgpt and Claude to do this task.
The final step is to send the generated brochure to another LLM for translation into the desired language.In our context, we decided to translate it into French.
- Tech: LLM, Anthropic, Gradio, BeautifulSoup
## Deal Finder
- Description: In this project we built an advanced Multi agent that subscribes to RSS feeds, check for a new opportunity deal (product), when the Multi agent finds a good deal it returns a notification containing the title, description, price, and url of the products it found.
- Tech: LLM, RAG, Langchain
## Insurance Chatbot
- Description: In this project, we built an AI insurance ChatBot to help respond to customers. In order to build our AI insurance ChatBot, we used an LMI that was combined with a knowledge base using the technique known as RAG. The first step is to use a knowledge base containing information about the insurance company. We extract important context from this knowledge base. We will then vectorize the data from the knowledge base to produce better queries for our data. To prevent the LLM from making a mistake and returning the wrong answer to the client, we instruct the LLM to return the answer if it doesn't exist in the knowledge base.
- Tech: LLM, GPT, RAG, ChromaDB
|
github_readmes
| null | null |
github_readmes_portfolio.md_1c3837cb49d6
|
# Portfolio Projects
## KnowBot
- Description: Knowbot is designed to be an intelligent assistant that can seamlessly interact with your organization’s internal knowledge base. Whether it's through voice or text queries, this assistant taps into your existing documents, policies, research papers, and other content to provide accurate, up-to-date, and contextually relevant answers. It blends retrieval-augmented generation (RAG) for data sourcing and a large language model (LLM) for sophisticated reasoning and answer generation.
- Tech: Python, Openai, Gradio, Whisper, LLM
## Multi-Agent Financial Advisor System
- Description: This project is a production-ready multi-agent AI financial advisory system designed to deliver holistic, personalized financial guidance.
It integrates investment advisory, tax optimization, and retirement planning into a unified intelligent platform.
The system leverages:
Specialized AI agents coordinated by an Orchestrator Agent
Real-time market intelligence
Advanced Retrieval-Augmented Generation (RAG) with hybrid search
Structured financial data pipelines
Educational visual generation using OpenAI Images
The advisor interprets a user’s full financial profile (income, assets, liabilities, goals, risk tolerance) and produces coherent, actionable financial plans while resolving conflicts between investment growth, tax efficiency, and long-term retirement objectives.
- Tech: Python, Gradio, Langchain, Vector-Database, Semantic Search, RAG
## AzureQbot
- Description: A modern React frontend for a Knowledge Base Chatbot hosted on Azure. Features a sleek chat interface with Markdown rendering, avatars, message timestamps, dark mode, and seamless integration with a Python backend API.
- Tech: React, FastAPI, Azure, QA, Knowledgebase
## AI Brochure Generator
- Description: In this project, we built a highly efficient brouchure generator using LLM. We also added the option of translating the generated brochure. The solution process can be divided into three main stages. To build our graphical interface we used gradio wich is an open-source Python framework that simplifies the creation of interactive web interfaces for machine learning models, APIs, or any Python function
The first step is to build a website scrapper that can retrieve the content of a given url website.
After scrapping the website, we'll send the useful website content to an LLM model. The LLM model will generate a brochure by summarizing and extracting the useful information. We choose Chatgpt and Claude to do this task.
The final step is to send the generated brochure to another LLM for translation into the desired language.In our context, we decided to translate it into French.
- Tech: LLM, Anthropic, Gradio, BeautifulSoup
## Deal Finder
- Description: In this project we built an advanced Multi agent that subscribes to RSS feeds, check for a new opportunity deal (product), when the Multi agent finds a good deal it returns a notification containing the title, description, price, and url of the products it found.
- Tech: LLM, RAG, Langchain
## Insurance Chatbot
- Description: In this project, we built an AI insurance ChatBot to help respond to customers. In order to build our AI insurance ChatBot, we used an LMI that was combined with a knowledge base using the technique known as RAG. The first step is to use a knowledge base containing information about the insurance company. We extract important context from this knowledge base. We will then vectorize the data from the knowledge base to produce better queries for our data. To prevent the LLM from making a mistake and returning the wrong answer to the client, we instruct the LLM to return the answer if it doesn't exist in the knowledge base.
- Tech: LLM, GPT, RAG, ChromaDB
|
github_readmes
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github_readmes_portfolio.md_1c3837cb49d6
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# Portfolio Projects
## KnowBot
- Description: Knowbot is designed to be an intelligent assistant that can seamlessly interact with your organization’s internal knowledge base. Whether it's through voice or text queries, this assistant taps into your existing documents, policies, research papers, and other content to provide accurate, up-to-date, and contextually relevant answers. It blends retrieval-augmented generation (RAG) for data sourcing and a large language model (LLM) for sophisticated reasoning and answer generation.
- Tech: Python, Openai, Gradio, Whisper, LLM
## Multi-Agent Financial Advisor System
- Description: This project is a production-ready multi-agent AI financial advisory system designed to deliver holistic, personalized financial guidance.
It integrates investment advisory, tax optimization, and retirement planning into a unified intelligent platform.
The system leverages:
Specialized AI agents coordinated by an Orchestrator Agent
Real-time market intelligence
Advanced Retrieval-Augmented Generation (RAG) with hybrid search
Structured financial data pipelines
Educational visual generation using OpenAI Images
The advisor interprets a user’s full financial profile (income, assets, liabilities, goals, risk tolerance) and produces coherent, actionable financial plans while resolving conflicts between investment growth, tax efficiency, and long-term retirement objectives.
- Tech: Python, Gradio, Langchain, Vector-Database, Semantic Search, RAG
## AzureQbot
- Description: A modern React frontend for a Knowledge Base Chatbot hosted on Azure. Features a sleek chat interface with Markdown rendering, avatars, message timestamps, dark mode, and seamless integration with a Python backend API.
- Tech: React, FastAPI, Azure, QA, Knowledgebase
## AI Brochure Generator
- Description: In this project, we built a highly efficient brouchure generator using LLM. We also added the option of translating the generated brochure. The solution process can be divided into three main stages. To build our graphical interface we used gradio wich is an open-source Python framework that simplifies the creation of interactive web interfaces for machine learning models, APIs, or any Python function
The first step is to build a website scrapper that can retrieve the content of a given url website.
After scrapping the website, we'll send the useful website content to an LLM model. The LLM model will generate a brochure by summarizing and extracting the useful information. We choose Chatgpt and Claude to do this task.
The final step is to send the generated brochure to another LLM for translation into the desired language.In our context, we decided to translate it into French.
- Tech: LLM, Anthropic, Gradio, BeautifulSoup
## Deal Finder
- Description: In this project we built an advanced Multi agent that subscribes to RSS feeds, check for a new opportunity deal (product), when the Multi agent finds a good deal it returns a notification containing the title, description, price, and url of the products it found.
- Tech: LLM, RAG, Langchain
## Insurance Chatbot
- Description: In this project, we built an AI insurance ChatBot to help respond to customers. In order to build our AI insurance ChatBot, we used an LMI that was combined with a knowledge base using the technique known as RAG. The first step is to use a knowledge base containing information about the insurance company. We extract important context from this knowledge base. We will then vectorize the data from the knowledge base to produce better queries for our data. To prevent the LLM from making a mistake and returning the wrong answer to the client, we instruct the LLM to return the answer if it doesn't exist in the knowledge base.
- Tech: LLM, GPT, RAG, ChromaDB
|
github_readmes
| null | null |
github_readmes_portfolio.md_1c3837cb49d6
|
# Portfolio Projects
## KnowBot
- Description: Knowbot is designed to be an intelligent assistant that can seamlessly interact with your organization’s internal knowledge base. Whether it's through voice or text queries, this assistant taps into your existing documents, policies, research papers, and other content to provide accurate, up-to-date, and contextually relevant answers. It blends retrieval-augmented generation (RAG) for data sourcing and a large language model (LLM) for sophisticated reasoning and answer generation.
- Tech: Python, Openai, Gradio, Whisper, LLM
## Multi-Agent Financial Advisor System
- Description: This project is a production-ready multi-agent AI financial advisory system designed to deliver holistic, personalized financial guidance.
It integrates investment advisory, tax optimization, and retirement planning into a unified intelligent platform.
The system leverages:
Specialized AI agents coordinated by an Orchestrator Agent
Real-time market intelligence
Advanced Retrieval-Augmented Generation (RAG) with hybrid search
Structured financial data pipelines
Educational visual generation using OpenAI Images
The advisor interprets a user’s full financial profile (income, assets, liabilities, goals, risk tolerance) and produces coherent, actionable financial plans while resolving conflicts between investment growth, tax efficiency, and long-term retirement objectives.
- Tech: Python, Gradio, Langchain, Vector-Database, Semantic Search, RAG
## AzureQbot
- Description: A modern React frontend for a Knowledge Base Chatbot hosted on Azure. Features a sleek chat interface with Markdown rendering, avatars, message timestamps, dark mode, and seamless integration with a Python backend API.
- Tech: React, FastAPI, Azure, QA, Knowledgebase
## AI Brochure Generator
- Description: In this project, we built a highly efficient brouchure generator using LLM. We also added the option of translating the generated brochure. The solution process can be divided into three main stages. To build our graphical interface we used gradio wich is an open-source Python framework that simplifies the creation of interactive web interfaces for machine learning models, APIs, or any Python function
The first step is to build a website scrapper that can retrieve the content of a given url website.
After scrapping the website, we'll send the useful website content to an LLM model. The LLM model will generate a brochure by summarizing and extracting the useful information. We choose Chatgpt and Claude to do this task.
The final step is to send the generated brochure to another LLM for translation into the desired language.In our context, we decided to translate it into French.
- Tech: LLM, Anthropic, Gradio, BeautifulSoup
## Deal Finder
- Description: In this project we built an advanced Multi agent that subscribes to RSS feeds, check for a new opportunity deal (product), when the Multi agent finds a good deal it returns a notification containing the title, description, price, and url of the products it found.
- Tech: LLM, RAG, Langchain
## Insurance Chatbot
- Description: In this project, we built an AI insurance ChatBot to help respond to customers. In order to build our AI insurance ChatBot, we used an LMI that was combined with a knowledge base using the technique known as RAG. The first step is to use a knowledge base containing information about the insurance company. We extract important context from this knowledge base. We will then vectorize the data from the knowledge base to produce better queries for our data. To prevent the LLM from making a mistake and returning the wrong answer to the client, we instruct the LLM to return the answer if it doesn't exist in the knowledge base.
- Tech: LLM, GPT, RAG, ChromaDB
|
github_readmes
| null | null |
github_readmes_portfolio.md_1c3837cb49d6
|
# Portfolio Projects
## KnowBot
- Description: Knowbot is designed to be an intelligent assistant that can seamlessly interact with your organization’s internal knowledge base. Whether it's through voice or text queries, this assistant taps into your existing documents, policies, research papers, and other content to provide accurate, up-to-date, and contextually relevant answers. It blends retrieval-augmented generation (RAG) for data sourcing and a large language model (LLM) for sophisticated reasoning and answer generation.
- Tech: Python, Openai, Gradio, Whisper, LLM
## Multi-Agent Financial Advisor System
- Description: This project is a production-ready multi-agent AI financial advisory system designed to deliver holistic, personalized financial guidance.
It integrates investment advisory, tax optimization, and retirement planning into a unified intelligent platform.
The system leverages:
Specialized AI agents coordinated by an Orchestrator Agent
Real-time market intelligence
Advanced Retrieval-Augmented Generation (RAG) with hybrid search
Structured financial data pipelines
Educational visual generation using OpenAI Images
The advisor interprets a user’s full financial profile (income, assets, liabilities, goals, risk tolerance) and produces coherent, actionable financial plans while resolving conflicts between investment growth, tax efficiency, and long-term retirement objectives.
- Tech: Python, Gradio, Langchain, Vector-Database, Semantic Search, RAG
## AzureQbot
- Description: A modern React frontend for a Knowledge Base Chatbot hosted on Azure. Features a sleek chat interface with Markdown rendering, avatars, message timestamps, dark mode, and seamless integration with a Python backend API.
- Tech: React, FastAPI, Azure, QA, Knowledgebase
## AI Brochure Generator
- Description: In this project, we built a highly efficient brouchure generator using LLM. We also added the option of translating the generated brochure. The solution process can be divided into three main stages. To build our graphical interface we used gradio wich is an open-source Python framework that simplifies the creation of interactive web interfaces for machine learning models, APIs, or any Python function
The first step is to build a website scrapper that can retrieve the content of a given url website.
After scrapping the website, we'll send the useful website content to an LLM model. The LLM model will generate a brochure by summarizing and extracting the useful information. We choose Chatgpt and Claude to do this task.
The final step is to send the generated brochure to another LLM for translation into the desired language.In our context, we decided to translate it into French.
- Tech: LLM, Anthropic, Gradio, BeautifulSoup
## Deal Finder
- Description: In this project we built an advanced Multi agent that subscribes to RSS feeds, check for a new opportunity deal (product), when the Multi agent finds a good deal it returns a notification containing the title, description, price, and url of the products it found.
- Tech: LLM, RAG, Langchain
## Insurance Chatbot
- Description: In this project, we built an AI insurance ChatBot to help respond to customers. In order to build our AI insurance ChatBot, we used an LMI that was combined with a knowledge base using the technique known as RAG. The first step is to use a knowledge base containing information about the insurance company. We extract important context from this knowledge base. We will then vectorize the data from the knowledge base to produce better queries for our data. To prevent the LLM from making a mistake and returning the wrong answer to the client, we instruct the LLM to return the answer if it doesn't exist in the knowledge base.
- Tech: LLM, GPT, RAG, ChromaDB
|
github_readmes
| null | null |
github_readmes_portfolio.md_1c3837cb49d6
|
# Portfolio Projects
## KnowBot
- Description: Knowbot is designed to be an intelligent assistant that can seamlessly interact with your organization’s internal knowledge base. Whether it's through voice or text queries, this assistant taps into your existing documents, policies, research papers, and other content to provide accurate, up-to-date, and contextually relevant answers. It blends retrieval-augmented generation (RAG) for data sourcing and a large language model (LLM) for sophisticated reasoning and answer generation.
- Tech: Python, Openai, Gradio, Whisper, LLM
## Multi-Agent Financial Advisor System
- Description: This project is a production-ready multi-agent AI financial advisory system designed to deliver holistic, personalized financial guidance.
It integrates investment advisory, tax optimization, and retirement planning into a unified intelligent platform.
The system leverages:
Specialized AI agents coordinated by an Orchestrator Agent
Real-time market intelligence
Advanced Retrieval-Augmented Generation (RAG) with hybrid search
Structured financial data pipelines
Educational visual generation using OpenAI Images
The advisor interprets a user’s full financial profile (income, assets, liabilities, goals, risk tolerance) and produces coherent, actionable financial plans while resolving conflicts between investment growth, tax efficiency, and long-term retirement objectives.
- Tech: Python, Gradio, Langchain, Vector-Database, Semantic Search, RAG
## AzureQbot
- Description: A modern React frontend for a Knowledge Base Chatbot hosted on Azure. Features a sleek chat interface with Markdown rendering, avatars, message timestamps, dark mode, and seamless integration with a Python backend API.
- Tech: React, FastAPI, Azure, QA, Knowledgebase
## AI Brochure Generator
- Description: In this project, we built a highly efficient brouchure generator using LLM. We also added the option of translating the generated brochure. The solution process can be divided into three main stages. To build our graphical interface we used gradio wich is an open-source Python framework that simplifies the creation of interactive web interfaces for machine learning models, APIs, or any Python function
The first step is to build a website scrapper that can retrieve the content of a given url website.
After scrapping the website, we'll send the useful website content to an LLM model. The LLM model will generate a brochure by summarizing and extracting the useful information. We choose Chatgpt and Claude to do this task.
The final step is to send the generated brochure to another LLM for translation into the desired language.In our context, we decided to translate it into French.
- Tech: LLM, Anthropic, Gradio, BeautifulSoup
## Deal Finder
- Description: In this project we built an advanced Multi agent that subscribes to RSS feeds, check for a new opportunity deal (product), when the Multi agent finds a good deal it returns a notification containing the title, description, price, and url of the products it found.
- Tech: LLM, RAG, Langchain
## Insurance Chatbot
- Description: In this project, we built an AI insurance ChatBot to help respond to customers. In order to build our AI insurance ChatBot, we used an LMI that was combined with a knowledge base using the technique known as RAG. The first step is to use a knowledge base containing information about the insurance company. We extract important context from this knowledge base. We will then vectorize the data from the knowledge base to produce better queries for our data. To prevent the LLM from making a mistake and returning the wrong answer to the client, we instruct the LLM to return the answer if it doesn't exist in the knowledge base.
- Tech: LLM, GPT, RAG, ChromaDB
|
github_readmes
| null | null |
github_readmes_portfolio.md_1c3837cb49d6
|
# Portfolio Projects
## KnowBot
- Description: Knowbot is designed to be an intelligent assistant that can seamlessly interact with your organization’s internal knowledge base. Whether it's through voice or text queries, this assistant taps into your existing documents, policies, research papers, and other content to provide accurate, up-to-date, and contextually relevant answers. It blends retrieval-augmented generation (RAG) for data sourcing and a large language model (LLM) for sophisticated reasoning and answer generation.
- Tech: Python, Openai, Gradio, Whisper, LLM
## Multi-Agent Financial Advisor System
- Description: This project is a production-ready multi-agent AI financial advisory system designed to deliver holistic, personalized financial guidance.
It integrates investment advisory, tax optimization, and retirement planning into a unified intelligent platform.
The system leverages:
Specialized AI agents coordinated by an Orchestrator Agent
Real-time market intelligence
Advanced Retrieval-Augmented Generation (RAG) with hybrid search
Structured financial data pipelines
Educational visual generation using OpenAI Images
The advisor interprets a user’s full financial profile (income, assets, liabilities, goals, risk tolerance) and produces coherent, actionable financial plans while resolving conflicts between investment growth, tax efficiency, and long-term retirement objectives.
- Tech: Python, Gradio, Langchain, Vector-Database, Semantic Search, RAG
## AzureQbot
- Description: A modern React frontend for a Knowledge Base Chatbot hosted on Azure. Features a sleek chat interface with Markdown rendering, avatars, message timestamps, dark mode, and seamless integration with a Python backend API.
- Tech: React, FastAPI, Azure, QA, Knowledgebase
## AI Brochure Generator
- Description: In this project, we built a highly efficient brouchure generator using LLM. We also added the option of translating the generated brochure. The solution process can be divided into three main stages. To build our graphical interface we used gradio wich is an open-source Python framework that simplifies the creation of interactive web interfaces for machine learning models, APIs, or any Python function
The first step is to build a website scrapper that can retrieve the content of a given url website.
After scrapping the website, we'll send the useful website content to an LLM model. The LLM model will generate a brochure by summarizing and extracting the useful information. We choose Chatgpt and Claude to do this task.
The final step is to send the generated brochure to another LLM for translation into the desired language.In our context, we decided to translate it into French.
- Tech: LLM, Anthropic, Gradio, BeautifulSoup
## Deal Finder
- Description: In this project we built an advanced Multi agent that subscribes to RSS feeds, check for a new opportunity deal (product), when the Multi agent finds a good deal it returns a notification containing the title, description, price, and url of the products it found.
- Tech: LLM, RAG, Langchain
## Insurance Chatbot
- Description: In this project, we built an AI insurance ChatBot to help respond to customers. In order to build our AI insurance ChatBot, we used an LMI that was combined with a knowledge base using the technique known as RAG. The first step is to use a knowledge base containing information about the insurance company. We extract important context from this knowledge base. We will then vectorize the data from the knowledge base to produce better queries for our data. To prevent the LLM from making a mistake and returning the wrong answer to the client, we instruct the LLM to return the answer if it doesn't exist in the knowledge base.
- Tech: LLM, GPT, RAG, ChromaDB
|
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