AI systems engineering
Architecture, deployment, local inference, evaluation, and research-to-product translation for AI systems.
An AI system has to fit the data, hardware, budget, and people operating it. I work across those layers, from a focused software implementation to infrastructure and deployment involving several teams.
From a problem to an implementation
Slow inference might be a model problem, a batching problem, or a hardware bottleneck. Unreliable results might start in the data pipeline. I investigate the actual failure before choosing what to change.
My work includes production data pipelines, LLM systems, and an in-house GPU experimentation lab. I can implement independently, join your engineers, or lead the team. Selected work describes my contribution to those systems.
Architecture & deployment
Production MLOps, local inference, evaluation, observability, CI/CD, and operational ownership need to be considered together. The right architecture depends on what you need to achieve and the resources you can provide.
Services, discovery, and hourly rates explain how we start. If you already know what needs attention, book a call or email me.
Headroom
This queueing model illustrates why spare capacity matters. It is an idealized M/M/1 queue, not a benchmark of a client system.