AI / ML Engineer
I build AI systems from the ground up mixing knowledge graphs, neural architectures, computer vision and get them out of the notebook.
Final-year M2 Data Science & AI @ Université Paris-Saclay · AI Engineer Intern @ Dassault Systèmes.
about
I'm a Software engineer with a big focus more AI/ML, who learns a thing by building it from the bottom. That's meant writing a Transformer tensor by tensor, hand-modelling an OWL ontology, wiring classical image processing into a CNN, and turning a research prototype into a deployed service. Different domains, same instinct: understand the problem, then make it survive contact with real data.
Most recently that's been retrieval and knowledge graphs at Dassault Systèmes and BNP Paribas — but the through-line across everything I've shipped is engineering, not a single framework. I care as much about clean pipelines and reliable evaluation as about the last point of accuracy.
Currently: Searching work · exploring Teyvat · Doja Cat on repeat !
experience
Built an end-to-end GraphRAG assistant over a skills ontology (ESCO/SKOS): natural-language to SPARQL, an ontology embedding pipeline in Java with an in-memory vector store, and a conversational panel inside the 3DEXPERIENCE Ontology Explorer.
Built a multi-agent RAG assistant for internal knowledge access — semantic search over 20K+ documents with BGE multilingual embeddings, FAISS indexing, and a GPT model for answer generation.
Built a document-clustering pipeline (TF-IDF + K-Means, scikit-learn, spaCy) over 500+ documents, surfacing recurring topics and the most-referenced resources.
Developed a Variational Recurrent Autoencoder in TensorFlow to detect anomalies in clients' historical records, reaching 94% accuracy and cutting manual data-cleaning effort.
skills
projects
Knowledge graphs, neural architectures, computer vision, applied risk modelling, representation learning — each one built rather than assembled from a tutorial.
Ask a question in plain language and get an answer straight from a graph. An LLM turns the question into SPARQL against a hand-modelled RDF ontology, the query runs locally, and a second pass turns the raw bindings back into a sentence. Same architecture as the assistant I built at Dassault Systèmes, rebuilt from scratch at a scale you can actually poke at.
The interactive demo lands here — ask a question in plain language, watch the SPARQL get generated and run against the graph.
A full Transformer built in PyTorch with no high-level libraries — multi-head self-attention, positional encoding, and autoregressive text generation, implemented to understand every tensor rather than to call one.
A hybrid computer-vision pipeline on MRI scans: classical preprocessing and segmentation feeding a CNN for detection and localization. Two approaches that are usually pitched against each other, working together.
Built at Hack4Future with Société Générale and won first place nationally: an AI-assisted credit workflow chaining document analysis, cross-document consistency checking, and ML fraud detection into a single risk decision.
A study on multi-model feature fusion: combining deep feature extraction with representation learning to push classification performance past what any single backbone reached on its own.
A deployed meal-planning application built on recommendation algorithms and REST APIs, designed around how people actually eat.
An intelligent student assistant bringing personalized recommendations, resource aggregation, and productivity tools into one app.
Engineering thesis: a multi-agent architecture for contextual reasoning and information retrieval, graded with high honors.
contact
Looking for a full-time AI/ML engineering role from September 2026. If your team works on retrieval, knowledge graphs, or getting models into production, I'd like to hear about it.