Freelancer profile translated to English.
Back to original languageAbout François
ML Engineer and PhD, I embraced LLMs from their inception. After 3 roles heavily focused on information retrieval, search engines (including web scale), and agentic search, I'm going freelance to support companies looking to internalize these technologies. Target project examples: RAG, agentic search, search engines to assist an AI, etc.
French
Native or bilingual
English
Fluent
German
Conversational
Can work on-site
Paris (up to 50km), Chantilly (up to 20km), Vernon (up to 20km), Strasbourg (up to 20km)
Experience
- LinkupMember of the Technical StaffTECHFebruary 2026 - Today (6 months)Contributions to the 'agentic retrieval' part of the search engine:
- Implementation of a learning-to-rank stack capable of easy iteration
- Challenging the agentic methodology for deriving a query from a complex user request
- End-to-end index vectorization: benchmarking embedding models on business corpus, writing RFC, and implementing the vectorization stack
- LeboncoinMachine Learning Engineer, Search Engine TeamMay 2024 - February 2026 (1 year and 9 months)Paris, FranceData scientist specializing in information retrieval, I am part of the team that maintains and improves the platform's search engine. My achievements:
- LLM pre-processing of listings to increase overall recall: keyword completion for short listings, LLM as SPLADE for sparse vector retrieval, PoC for 'anti-SEO' document cleaning
- Reranking overhaul for the 'auto/moto' vertical
- Creation of a judgment list generation pipeline tailored to Le Bon Coin's needs
- Participation in a vector DB benchmark by setting up a Vespa cluster at Le Bon Coin's traffic scale
- Contribution to the ML guild with technological watch
- QwantData Scientist / Machine Learning EngineerSeptember 2019 - January 2024 (4 years and 4 months)Paris, FranceSteering the creation and maintenance of QwantML, an internal machine learning library for industrial use• - Implementation of the data and model lifecycle (MLFlow, DVC)• - Implementation or integration of state-of-the-art ranking models (JAX, PyTorch, LightGBM) Creation, deployment, and daily use of the main information retrieval pipeline on a Vespa index:• - Creation and cleaning of information retrieval datasets (PySpark, K8S, ArgoWorkflow)• - Fitting ranking models via QwantML and deployment on Vespa (ONNX, LightGBM)• - Creation of a new rank1 method, deployment, and paper submission at BerlinBuzzWord23 (JAX)• - Quality measurement, reproducibility, interpretability of models (LLM, MLFlow, Hydra, Captum) Contribution to an ML/NLP pipeline for assigning a query to a relevant topic index out of 1k indexes• - Creation and cleaning of query datasets with their topics (PySpark, ArgoWorkflow)• - Vectorization of queries and vector matching against 1k indexes (HF, Vespa)• - Construction of meta-indexes representing the acceptable query scope for each index (FAISS) NLP exploration to increase ranking recall on a 10B docs index via document expansion• - Mentoring an M2 internship on the industrialization of sparse+dense NLP ranking (HF, SPLADE)• - Document categorization and document expansion (HF, SPLADE) Explorations on RAG with hybrid sparse/dense search (Llama-index, Vespa, RAG, LLM) Steering a project to maintain a satisfactory quality level of the main Qwant index• - Training and deployment of a spam removal score for 5B items in the index (SKLearn, HF, Vespa)• - Mentoring and deployment of an M2 internship on prioritizing documents for crawling (SKLearn)• - Development and deployment in containerized environments using Docker, Kubernetes, and ArgoWorkflow
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Education
- PhD, Mathematics and StatisticsTelecom ParisTech2017Doctorat, Mathématiques et statistiques
- Engineering Diploma from ENSAI, Statistical EngineeringEcole nationale de la Statistique et de l'Analyse de l'Information2013Diplôme d'ingénieur de l'ENSAI, Génie Statistique