AI Engineer building practical GenAI and agentic systems

I build practical AI applications combining LLMs, retrieval, agentic workflows and production-ready APIs.

Mobility: Île-de-France · Grand Est

Hobbies including probability, chess, poker, basketball and video games

Experience across

  • IADF Finance logo
  • TNP Consultants logo
  • Présanse logo
  • SUEZ logo
  • Gouvernement de Monaco logo
  • METT logo

Portfolio note

I'm currently connecting live demos and detailed technical case studies.

01Featured projects

Systems built for real problems

Health

Occupational Health RAG Assistant

Enterprise RAG

Demo in progress

Evidence-grounded assistant for occupational-health documentation, with cited retrieval and answer validation.

  • Python
  • FastAPI
  • RAG
  • pgvector
  • RAGAS

Recruitment

AI Talent Matching Platform

Agentic Workflows

Demo in progress

Agentic workflow for structuring requirements, ranking candidates and explaining recommendations.

  • Gemini
  • LangGraph
  • FastAPI
  • Supabase
  • pgvector

Health

Clinical Document Intelligence

Document AI

Demo in progress

AI workflow for structured extraction, document comparison and expert-review support.

  • LLMs
  • Pydantic
  • structured extraction
  • agentic review

Customer Experience

Real-Time Customer Intelligence

Streaming NLP

Demo in progress

Streaming NLP dashboard for detecting sentiment shifts, emerging themes and anomalies.

  • Python
  • NLP
  • embeddings
  • clustering
  • LLM insights
02Technical capabilities

Core capabilities

Agentic Workflows

  • LangGraph
  • tool calling
  • human-in-the-loop
  • automation

RAG & Retrieval

  • chunking
  • embeddings
  • hybrid retrieval
  • pgvector

LLM Engineering

  • OpenAI
  • Gemini
  • Mistral
  • structured outputs

Backend & APIs

  • Python
  • FastAPI
  • PostgreSQL
  • Docker

Evaluation & Reliability

  • RAGAS
  • LLM-as-a-Judge
  • monitoring
  • cost & latency
03Engineering approach

From problem to production

  1. 01→

    Understand

    Frame the real problem, users and useful outcome.

  2. 02→

    Prototype

    Ship a working system end to end first.

  3. 03→

    Evaluate

    Measure quality, cost and limitations.

  4. 04

    Productize

    Turn what works into reusable components.

Let's build useful AI systems

Interested in practical AI collaborations and production-focused engineering.

Contact me