Description

AI Engineer specialized in LLM applications, Retrieval-Augmented Generation (RAG), and intelligent automation.

I design and deploy AI systems that transform data into actionable insights and scalable products. My expertise includes building LLM-powered applications, AI chatbots, and data pipelines for real-world business use cases.

Currently working as an AI Engineer intern at Institut Pasteur, I develop machine learning solutions applied to biomedical and speech datasets, combining advanced modeling with production-ready engineering.

Core expertise:

• Large Language Models (LLM) integration

• Retrieval-Augmented Generation (RAG)

• Vector Databases (FAISS, Pinecone, Chroma)

• AI Chatbots & Conversational Agents

• Python / Machine Learning / NLP

• Data pipelines & automation

• API integration and backend systems

Tech stack:

Python • PyTorch • TensorFlow • LangChain • OpenAI API • Hugging Face • Docker • Git • SQL

What I can help you build:

• AI chatbots and assistants

• Knowledge-base search systems (RAG)

• AI automation workflows

• Custom ML/NLP solutions

• Data analysis and predictive models

If you're looking to integrate AI into your product or automate complex processes, I can help you design robust and scalable solutions.

Languages

French
Native or bilingual
English
Native or bilingual

Workplace preferences

Can work on-site

Paris (up to 50km)

Institut Pasteur
AI Engineer – Medical AI Systems
RESEARCH
March 2026 - Today (5 months)
Paris, France
- ENTIA medical chatbot: developed a conversational assistant for ENT physicians (RAG/LightRAG) with multi-agent orchestration (router + specialized agents), a knowledge graph, and BGE-reranker-v2-m3
- ORL-Bench — selecting the best model & RAG for ENTIA: full-stack platform (React, NestJS, FastAPI) to train, test, and compare LLMs on 600 clinical cases; goal: choose the optimal configuration to integrate into the ENTIA product
- Evaluation & POC: RAG pipeline (FAISS, embeddings, jina-reranker); comparison of 3 strategies (direct / RAG / RAG+rerank); automated KPIs (scientific_score), export to JSON/PDF/JSONL; validation on 30 cases (100% pass rate)
- Multi-LLM integration: connected multiple models (Mistral, Gemini, DeepSeek, Qwen, OpenAI) via LiteLLM; prompt engineering and performance comparison to guide deployment decisions
- MLOps & industrialization: Docker/Kubernetes, GitLab CI/CD (Kaniko/Harbor), Redis/RQ async jobs, JWT auth, circuit breaker; technical documentation and feedback sessions with domain experts
Vector DB knowledge base search Langchain Retrieval-Augmented Generation (RAG) Chatbot IA
OCP & YouCode
Full-Stack Developer / Data Analyst
RETAIL (LARGE RETAILERS)
May 2024 - September 2024 (4 months)
Morocco
• • End-to-end application design using UML, wireframes and system architecture
• • Web development using React and Node.js
• • Implementation of REST APIs and MySQL databases
• • Automation of data pipelines connecting APIs, databases and Power BI
• • Development of decision dashboards with business KPIs
• • Collaboration using Git/GitHub in Agile environment
React.js Node.js Python JavaScript MySQL
OCP Group
AI Data Analysis Pipeline - Predictive Maintenance
RAW MATERIALS INDUSTRY
June 2023 - September 2023 (3 months)
Morocco
• • Data preprocessing and analysis using Pandas and NumPy for maintenance my purposes.
• • Feature engineering and predictive modeling in Python
• • Implementation of Random Forest and Logistic Regression
• • Model evaluation using statistical performance metrics
• • Data visualization using Matplotlib
Microsoft Power BI Random Forest Machine learning Deep Learning ETL (Extract, Transform, Load) Processes