I’m a senior machine learning engineer with a research background. My work concentrates on three areas:

1. Production ML systems: training + evaluation pipelines, feature/data quality, reproducibility.
2. Performance & cost: profiling, workload consolidation, pragmatic refactoring of critical paths.
3. Reliability & observability: clear metrics, failure analysis, reducing variance in model behaviour and deployments.

Previously, I did research in machine learning and feature relevance with a focus on reproducibility and efficient experimentation (high‑performance computing roots). I still care about lean experiment design, versioned data, and traceable results.

Selected themes I keep returning to:

• Simplifying overgrown internal platforms instead of adding another layer.
• Automating the boring parts of evaluation so human review time is spent on edge cases.
• Treating model + data + infra changes as one unit when reasoning about regression risk.

Outside work: strength training, mountain trails, strategy / systems games, and the occasional attempt to cook something plant‑based that isn’t another lentil variant.