I'm a software engineer at Age Bold, where I build healthcare systems that connect Medicare members to preventive care.
Most of my work is on large-scale backend system and real-time ML services. I build pipelines that ingest and reconcile health plan data at high volume, and the serving stack that turns that data into real-time, personalized recommendations. I care about the whole path: how data enters the system, how a model uses it under a tight latency budget, and how user feedback returns to make the next prediction better.
I like the place where machine learning meets software engineering: the work it takes to turn a model into a system that people can depend on. That means serving under a latency budget, data pipelines that stay correct at scale, evaluation that matches what users experience, and feedback loops that make the model better over time.