ML Systems Engineer building end-to-end, production-ready machine learning systems.
I design, train, deploy, and monitor ML models with a focus on reproducibility, scalability, and real-world usability.
- Production-grade ML pipelines
- FastAPI-based inference services
- Dockerized deployments
- Experiment tracking with MLflow
- CI/CD for ML workflows
Python • SQL • scikit-learn • Keras
FastAPI • Docker • MLflow • GitHub Actions
Streamlit • Hugging Face • Linux
I don’t just train models — I ship ML systems.