You''ll own the serving platform end-to-end: onboarding newly released models, measuring quality and performance equivalence across serving configurations, scheduling workloads across tenants, and continuously improving latency, throughput, utilization, reliability, and cost across the inference fleet. Measure and monitor quality equivalence across serving configurations, including KV cache quantization, speculative decoding acceptance thresholds, precision choices, and model routing, so in-house serving can be trusted to match reference-model quality on production workloads.