I BuildProduction ML

I am a Machine Learning & Data Engineer specializing in production systems for high stakes healthcare environments. My work sits at the intersection of predictive modeling, systems architecture, and clinical impact building infrastructure that doesn't just process data, but supports decisions that affect real lives.

My core focus is end-to-end ML pipeline architecture: from raw data ingestion through Medallion Architecture (Bronze → Silver → Gold) to recall optimized models in production. I build with Databricks, PySpark, XGBoost, and FastAPI, and I care deeply about the unglamorous fundamentals data quality, patient-isolated splits, drift detection, and threshold engineering that holds up in the real world.

I approach healthcare ML with a bias toward reliability over novelty. Whether designing fall risk prediction systems, multi criteria scoring frameworks (MCDA), or backtesting pipelines for delayed-label data, I optimize for what matters in regulated environments: explainability, reproducibility, and measurable outcomes.

Before specializing in ML, I built across the full stack agentic AI workflows with LangChain, pixel-perfect frontends in Next.js, and production mobile apps. That breadth shapes how I think: I never build models in a silo; I design systems the way a systems architect would, with the entire data-to-decision chain in view.