Machine Learning Engineer with an MSc in Physics. I build the tooling that keeps ML systems reliable in production: monitoring, data quality checks, drift detection. Stack: Python, Airflow, Docker, Kubernetes. Example: a dataset-gap tool that keeps contaminated data out of training before it hurts the model.
Curious? My CV is here.
I want remote contractor roles with hands-on technical work. I like organizing things: a closet, a trip, an issue list, a codebase. The goal is always the same: efficiency, clarity, reliability.
Technical notes on MLOps and ML reliability in production. This page is the professional brain. The stories live on the other page — that's the soul.