Pierre-Alexandre
Broux

Situation: The company needed to automate reverse documentation of existing codebases (legacy or poorly documented) to improve comprehension and maintainability.

Task: Develop the backend and worker components of an AI-assisted reverse documentation tool (Retro-Doc), first internally then open-sourced, within a 4-contributor team.

Actions:

• FastAPI backend: authentication (OIDC / Microsoft Entra ID), CRUD, and SSE streaming (tokens + tool events).
• Design of an agentic RAG agent (LangGraph) with 7 custom tools: hybrid search on Azure AI Search, tree navigation, code/documentation reading, and AST/CFG/DFG graph traversal. Multi-turn short-term memory with MongoDB checkpointing and branching.
• Production agent resilience: LLM-agnostic architecture (Mistral AI, OpenAI, Anthropic), cross-provider fallback, retries, context summarization, and call limits.
• Autonomous multi-step analysis agent (DeepAgents) with sub-agents and structured output, producing a report exportable to PDF.
• Worker (Azure Durable Functions): fan-out/fan-in pipeline extracting AST/CFG/DFG graphs (industrialization of a prototype, extensible by language), LLM-based documentation generation, and indexing into the Azure AI Search vector store.
• Design of CI/CD pipelines: GitLab CI/CD (internal), then GitHub Actions (open source) — QA, testing, secret scanning, and tag-based automated releases.

Result: Automated the analysis, documentation, and natural-language querying chain, released open source.

Stack: Python, FastAPI, LangChain, LangGraph, DeepAgents, Mistral AI, OpenAI, Anthropic, Microsoft Azure, Azure AI Search, Azure Durable Functions, Azure Blob Storage, Cosmos DB (MongoDB), Beanie, Pydantic, Pytest, Docker, Git, GitLab CI/CD, GitHub Actions.

Situation: The company wanted to develop an internal chatbot to support and automate certain business tasks.

Task: Design, develop, and optimize the chatbot's features.

Actions:

• Implemented a multi-format data extraction feature (PDF and DOCX) using LangChain.
• Developed a multilingual document translation feature via Azure.
• Improved LLM system prompts and the logic of existing features to increase response relevance.
• Mentored team members, supporting their skill development and ensuring deliverable quality.
• Optimized GitLab CI/CD pipelines by applying MLOps best practices.
• Documented algorithm design to facilitate knowledge sharing and ensure reproducibility.

Result: Reduced CI/CD pipeline execution time by 50% and increased internal user adoption of the chatbot.

Stack: Python, FastAPI, LangChain, LangGraph, Mistral AI, OpenAI, Hugging Face, Asyncio, Microsoft Azure, Docker, Pydantic, Pytest, Git, GitLab CI/CD, MongoDB, Beanie, JavaScript.

Situation: Entrepreneurial initiative to launch a SaaS solution aimed at reducing the time spent in traditional meetings.

Task: Design an asynchronous meeting platform incorporating best practices to optimize time management.

Actions:

• Complete solution development with Django (Python), Htmx and Tailwind CSS.
• Deployment and management of infrastructure on Amazon Web Service (AWS) and DigitalOcean.
• PostgreSQL database management.
• Application of DevOps best practices such as CI/CD and testing (unit, integration and system tests).
• Documenting algorithm designs to facilitate knowledge sharing and ensure reproducibility.

Result: Significant reduction in the number and duration of meetings, with a significant improvement in user productivity.

Stack: Python, Django, Amazon Web Service, Docker, Pytest, Tailwind CSS, Htmx, DigitalOcean, Git, Github Actions, PostgreSQL, JavaScript.

Situation: The company wanted to create an artificial intelligence (AI) laboratory dedicated to the development of specialized solutions for recognizing emotions from text (NLP) and speech.

Task: Designing a robust, scalable infrastructure aligned with the company's vision, while implementing state-of-the-art machine learning models for specific applications.

Actions:

• Setting up an infrastructure using MLOps best practices.
• Data collection, transformation and annotation.
• Development and deployment of deep learning models (PyTorch).
• Integration and adaptation of large language models (LLM).
• Prototyping intelligent agents with LangGraph et LangChain.
• Managing and optimizing computing resources.
• Supervision of collaborators to ensure consistency of developments. Algorithm documentation.

Result: Setting up an operational laboratory with optimized MLOps workflow, enabling rapid deployment of AI products.

Stack: Python, Hugging Face, OpenAI API, LangChain, LangGraph, ZenML, PyTorch, Gradio, MLflow, Qdrant, Git, Github Actions, Amazon Web Service (AWS), NumPy, Pandas, Pytest, Seaborn, Asyncio, Docker, Django, FastAPI, Tailwind CSS, Htmx, JavaScript.

Situation: The company wanted to develop artificial intelligence (AI) products specialized in natural language processing (NLP) for its customers.

Task: Implementing and adapting state-of-the-art machine learning models for specific applications, while supervising projects and collaborators.

Actions:

• Design and development of Python algorithms for Named Entity Recognition (NER), exploiting Large Language Models (LLMs) such as BERT and RoBERTa.
• Supervision of collaborators, ensuring their development and the quality of deliverables.
• Advising on the choice and implementation of a high-performance infrastructure tailored to project needs.

Result: Delivery of AI solutions tailored to business needs.

Stack: Python, PyTorch, PyTorch Lightning, Docker, Git, Jupyter, Seaborn, NumPy, Pandas.

Situation: The laboratory wanted to analyze historical French archives to extract usable information.

Task: Designing artificial intelligence (AI) algorithms to automate the analysis of ancient documents.

Actions:

• Development in Python of algorithms integrating optical character recognition (OCR), automatic transcription (ASR) and named entity recognition (NER).
• Implementation of a pipeline integrating different technologies (OCR, ASR, NER) to automate complete archive processing.
• Documenting algorithm designs to facilitate knowledge sharing and ensure reproducibility.

Result: Delivery of tools for better understanding and use of period documents, facilitating historical research and the enhancement of archives.

Stack: Python, PyTorch, Keras, Tensorflow, SLURM, Git, Scikit-learn, NumPy, Jupyter.

Situation: The company wanted to help documentalists in their work of annotating audiovisual data, where the growing volume of archives requires automation solutions.

Task: Developing artificial intelligence (AI) diarization algorithms to automate speaker annotation.

Actions:

• Development in Python of diarization algorithms taking human intervention into account.
• Active contribution to two Open-Source Python projects (S4D and SIDEKIT) specializing in speaker recognition and diarization.
• Development of an evaluation metric to accurately determine the performance of human-assisted diarization AI algorithms.
• Publishing results in the form of scientific articles and present them at international conferences.
• Working in an international context in English, collaborating effectively with non-French-speaking colleagues and partners.
• Documenting algorithm designs to facilitate knowledge sharing and ensure reproducibility.

Result: Delivery of a tool to improve the annotation speed of audiovisual data, facilitating the work of documentalists.

Stack: Python, Jupyter, Git, SLURM.

An open-source TypeScript, React and Node.js extension for Raycast dedicated to KeePassXC and used by over 4,500 users.

An open-source Python AI/ML toolkit dedicated to speaker diarization.