At Bourse des Crédits, I engineered a hybrid scoring engine that combines business rules, Gradient Boosting, and explainability to prioritize client files and recommend insurance offers. I deployed it as a FastAPI and Docker microservice with MLflow retraining pipelines.
I also integrated SHAP attributions with a locally hosted Qwen 2.5 model to generate French explanations, validated through an automated LLM-as-a-Judge pipeline.
At the Data Engineering & Semantics Research Unit (FSS), I architected a real-time video-indexing pipeline using Apache Kafka and FAISS. I benchmarked quantized vision-language models, with Qwen-VL 2.5 7B achieving 76.4% accuracy.
At LaRINa Research Laboratory, I built and shipped the AlmBark ID mobile application in Flutter, with an on-device vision pipeline. My projects also include a self-correcting multimodal RAG system and an autonomous API and code test-generation agent.

