Packaging, optimizing, and serving ML and LLM models in production. Spans containerization, GPU scheduling, inference optimization (quantization, batching, page
Model deployment and serving skill puts AI on the wire: inference servers (vLLM-class), GPU capacity planning, quantization for cost, autoscaling, and the latency/throughput engineering that turns model weights into reliable, affordable endpoints.
Cost-driven growth: as inference becomes a major budget line, engineers who cut serving costs while holding SLOs deliver provable money, among the most defensible infra niches in AI.
At sustained volume with stable tasks (cost), data-control mandates, or latency demands APIs can't meet: priced honestly against the ops you're adopting. Hybrid routing is the common end-state.
Usually batching plus quantization on right-sized models: most workloads over-provision capability. Measure tokens-per-dollar per task before buying bigger GPUs.