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.
Prototype CUDA projects on Colab, then move to persistent cloud GPUs for larger workloads and reproducible development.
Andrew Ng's four-skill map, condensed — then what each skill looks like when the product is a GPU pipeline on a fixed power budget rather than an LLM web app.
What git flow release finish actually does, one command at a time — plus a checklist and the recovery steps for when one is missed.
Happy to talk about computer vision and ML infrastructure.