I started in software engineering but moved into machine learning to work on systems that go beyond deterministic logic and adapt based on data. My focus is on building end-to-end ML systems, from data pipelines and feature engineering to model training, evaluation, and production deployment. What sets me apart is that I treat ML as a systems problem, not just modeling. I’ve worked on integrating models into scalable applications, optimizing inference, and setting up monitoring to ensure models perform reliably in production. I’m particularly interested in applying ML to real-world problems where performance, scalability, and measurable impact matter as much as model accuracy.
No skills.