Pierre-Alexandre
Broux

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 CI/CD, 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 CI/CD, 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 2,500 users.

An open-source Python machine learning toolkit dedicated to speaker diarization.