AI systems engineering
Architecture, deployment, local inference, evaluation, and implementation of 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
Deployment decisions affect how you evaluate a model, observe failures, release changes, and recover when something goes wrong. Local inference adds hardware and capacity constraints. I work through those decisions with the people who will build and operate the system.
Services and rates cover implementation, technical leadership, and discovery. To discuss the work, book a call or email me.
Headroom
Spare capacity changes how a system responds to more traffic. This idealized M/M/1 queue compares two starting points under the same 10% increase in arrival rate. It is not a benchmark of a client system.