ml / ai engineer
Building things that learn. Working across the full ML stack — data, training, evaluation, and serving at scale.
Building ML infrastructure at work — focused on LLM evaluation pipelines and deployment patterns that hold up in production. On the side: writing more, shipping personal projects, reading broadly across systems and inference optimization.
Nothing here yet.