Machine Learning Engineer in training | AI & Data Specialization
Final-year engineering student at CESI École d'Ingénieurs
I design and ship end-to-end ML systems: data pipelines, model training, evaluation, and API deployment. My focus areas are deep learning for computer vision & time series, NLP/RAG systems, and MLOps.
| Project | What it does | Stack | Results |
|---|---|---|---|
| 🛰️ Urban Change Detection | Siamese U-Net (ResNet34 encoder) detecting building changes between two satellite images, with a live demo | PyTorch, TorchGeo, Gradio | F1 0.536 on LEVIR-CD+ (+464% vs non-ML baseline) · Live demo 🤗 |
| Transformer-based model predicting aircraft trajectories, served via REST API | PyTorch, Transformers, FastAPI, Docker | Deployed inference API | |
| ⚙️ Predictive Maintenance / RUL | CNN-LSTM hybrid predicting Remaining Useful Life of turbofan engines | TensorFlow/Keras, NASA C-MAPSS | RMSE 37.74 cycles + scientific poster |
| 🧠 Mouse Behavior ML Baselines | Full supervised-learning pipeline for behavioral analysis, research @ CERVO Brain Research Centre | Python, scikit-learn, CV | Academic research project |
| 🎓 NovaCampus RAG Assistant | Retrieval-Augmented Generation assistant inside a microservices academic ERP | Qdrant, NestJS, LLM APIs, Docker | 5-layer microservices architecture |
| 🕹️ RL Pacman Agent | Reinforcement learning agent trained to play Pacman | Python, RL | Learning project |
- Master's degree in Computer Science, AI & Data specialization, CESI École d'Ingénieurs (2026)
- Research experience in Computer Vision, CERVO Brain Research Centre, Université Laval, Québec 🇨🇦
- Previous internships in software engineering and cybersecurity
💬 Open to discussing ML engineering, MLOps, or research opportunities. Reach out anytime.

