Pol QUIMERC'H.
Work-study Master's student, I build Machine Learning systems with concrete, measurable impact.
I started out in full-stack development before moving into AI engineering. I hold a professional bachelor's degree and am now specialising in Machine Learning and Deep Learning through a work-study Master's (RNCP level 7) at EPSI. Alongside it, I am completing three years as a data / ML apprentice at Eureden, an agricultural cooperative.
I work on applied AI and care about the engineering behind it, from exploring the data to running a reliable system in production. I am looking for a permanent position in Lyon, France, in R&D, Data Science or ML engineering, available from October 2026.
An open-source MLOps project that takes the model from a notebook to a FastAPI service on AWS, with automated retraining, drift detection (PSI) and Prometheus monitoring.
At Eureden, I industrialised a critical manual process with a serverless data pipeline (AWS Lambda, S3). It projects volumes over a rolling 36 months and supports scenario simulation.
I built a Sentinel-2 pipeline from scratch (STAC, COG partial reads, NDVI/EVI/NDWI) to enrich XGBoost yield models, then ran an ablation study to measure the gain.
Full case studies, with charts and production screenshots, are on the French projects page.
Prefer to ask directly? The assistant at the bottom right answers in English, about my background, projects or the role I am after.