10/13/2026 updated
100 % available
Senior AI Engineer & ML Tech Lead | Generative AI, LLMs, RAG, MLOps
CESSON-SEVIGNE, France
Only remote
Actuarial Science - Ensae ParisTechAbout me
With 13 years of experience in AI and a strong background in ML engineering, I specialize in data science and AI engineering. I have successfully led 20 ML projects from R&D to production. Over the past years, I've focused on generative AI and RAGs, to implement cutting-edge AI solutions.
GCPLangChainPython (Programming Language)SQL DatabasesJupyter NotebookPyTorchPySparkScikit LearnAWS Data Analytics
Generative AI & LLM Engineering
Expertise in building and fine-tuning Large Language Models (LLMs) for various use cases including text correction, rephrasing, and intelligent document processing. Hands-on experience with RAG systems, prompt engineering, vLLM, Qwen, Llama, LangChain, LlamaIndex, and LangGraph for production-ready AI solutions.
Machine Learning & Data Science
Strong background in developing and deploying ML algorithms across multiple domains including fraud detection, anomaly detection, cybersecurity, healthcare, and insurance. Experience leading ML projects from R&D to production, covering supervised and unsupervised learning techniques.
MLOps & Cloud Infrastructure
Proficiency in deploying and orchestrating scalable ML inference services on AWS, GCP, and Azure using Docker, FastAPI, and n8n. Implementation of production monitoring pipelines with Google BigQuery and Prometheus, as well as GPU optimization through batching, quantization, and asynchronous orchestration.
Python Programming
Advanced Python development for AI and data science applications, including building scalable pipelines, inference services, and agentic AI platforms.
PyTorch
Use of PyTorch for fine-tuning LLMs and developing deep learning models for NLP and other ML tasks.
SQL & Data Engineering
Solid knowledge of SQL and data engineering practices for managing large-scale datasets and supporting ML workflows.
AI-assisted Coding
Proficiency in leveraging AI-assisted coding tools to accelerate development and improve code quality in AI and ML projects.
OCR & Document Intelligence
Design and implementation of high-throughput OCR pipelines for large-scale document indexing, ingestion, and structured data extraction, including migration to Microsoft Azure Document Intelligence.
Technical Leadership
Experience leading data science and ML engineering teams, defining AI use-case roadmaps, and communicating AI strategies to C-level executives to align initiatives with business goals.
Expertise in building and fine-tuning Large Language Models (LLMs) for various use cases including text correction, rephrasing, and intelligent document processing. Hands-on experience with RAG systems, prompt engineering, vLLM, Qwen, Llama, LangChain, LlamaIndex, and LangGraph for production-ready AI solutions.
Machine Learning & Data Science
Strong background in developing and deploying ML algorithms across multiple domains including fraud detection, anomaly detection, cybersecurity, healthcare, and insurance. Experience leading ML projects from R&D to production, covering supervised and unsupervised learning techniques.
MLOps & Cloud Infrastructure
Proficiency in deploying and orchestrating scalable ML inference services on AWS, GCP, and Azure using Docker, FastAPI, and n8n. Implementation of production monitoring pipelines with Google BigQuery and Prometheus, as well as GPU optimization through batching, quantization, and asynchronous orchestration.
Python Programming
Advanced Python development for AI and data science applications, including building scalable pipelines, inference services, and agentic AI platforms.
PyTorch
Use of PyTorch for fine-tuning LLMs and developing deep learning models for NLP and other ML tasks.
SQL & Data Engineering
Solid knowledge of SQL and data engineering practices for managing large-scale datasets and supporting ML workflows.
AI-assisted Coding
Proficiency in leveraging AI-assisted coding tools to accelerate development and improve code quality in AI and ML projects.
OCR & Document Intelligence
Design and implementation of high-throughput OCR pipelines for large-scale document indexing, ingestion, and structured data extraction, including migration to Microsoft Azure Document Intelligence.
Technical Leadership
Experience leading data science and ML engineering teams, defining AI use-case roadmaps, and communicating AI strategies to C-level executives to align initiatives with business goals.
Experience
Professional experience10+ years
Freelancer since2019
Languages
EnglishFluentFrenchNative speaker
Project history
Built a scalable paraphrasing pipeline using vLLM with Qwen and Llama 3 for high-throughput, low-latency inference. Implemented prompt engineering, decoding optimization, and semantic evaluation pipelines. Optimized GPU utilization and serving infrastructure through batching, quantization, and asynchronous orchestration.
Built scalable LLM pipelines for legal document preprocessing and analysis. Developed RAG systems for intelligent database querying and retrieval workflows. Designed high-throughput OCR pipelines for large-scale document indexing and ingestion. Deployed and orchestrated AI workflows using n8n and Microsoft Azure. Contributed to modular, reusable GenAI architectures for production-ready AI solutions.
Built OCR pipelines for pharmaceutical contractual document preprocessing and structured data extraction. Developed an end-to-end RAG pipeline for intelligent retrieval across large document databases. Designed an agentic AI platform using LangChain with a Node.js frontend. Led the migration from Python/Tesseract-based processing to Microsoft Azure Document Intelligence and modern LLM orchestration frameworks.