High-Performance ML & Computer Vision · Heterogeneous AI Systems
I'm a machine learning engineer focused on computer vision and inference at scale — taking models from research code to production across GPUs, edge accelerators, and everything in between. I care about the parts most people skip: latency budgets, memory footprints, and pipelines that keep working after the demo ends.
Outside of shipping, I write about deployment tradeoffs and heterogeneous compute on the blog below.
What git flow release finish actually does, one command at a time — plus a checklist and the recovery steps for when one is missed.
Building an engineering contract that makes the model a replaceable part — constraints, handoff packets, enforced boundaries, and what delegation really costs.
A practical workflow for turning product intent into versioned specifications, implementation plans, executable validation, and repeatable AI-assisted delivery.
Happy to talk about computer vision and ML infrastructure.