AI infrastructure engineer with fifteen years in production operations, seven of them as a Site Reliability Engineer. I work on the reliability, performance and cost of AI/ML platforms running at scale in a regulated environment — AWS, Kubernetes, and the GPU-backed model serving behind them.
My specialism is the inference layer: what a model costs to serve, where latency originates, and how a serving stack behaves under real concurrency rather than in a demo.
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LLM performance benchmarking
Designed, built and operated a distributed load-testing framework — Locust master and workers on ECS — generating around 10,000 concurrent streaming sessions against models running on A100 and H100 hardware. Measured time to first token, time to first 100 tokens, sustained token throughput and streaming stability under load, and turned the results into capacity and sizing guidance.
The serving stack was owned by a partner team. The harness, the methodology and the analysis were mine.
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Platform reliability
SLOs, error budgets, incident response and capacity planning for production AI/ML services on EKS, SageMaker, EMR and Databricks — including the on-call rotation that carries them.
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Agentic operations tooling
Platform layer for an agentic incident-triage system: MCP servers, multi-tool orchestration, and the permission model governing what an agent is allowed to do against production systems.
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Current focus
Kubernetes-native inference — vLLM, llm-d and the Gateway API Inference Extension — and the routing, batching and KV-cache decisions that determine cost per million tokens.