Дополнительно
You will own how workloads scale and where they land — autoscaling to demand (up under load, down to zero when idle) and a single placement policy expressing region, compliance regime, and capacity preference, with compliance-bound workloads given right-of-way on sensitive capacity
You will make production inference reliable by default — every request reaches a healthy replica, rolling deploys never drop traffic, region-aware routing with multi-region / active-active and fallback as first-class policy, and health-aware recovery from stuck or bad replicas
You will build the release engine beneath safe rollouts — the traffic-shifting that powers canary/shadow/A/B, warm-ups, drain, and probes
You will push the cost/performance frontier for serving AI at scale — latency, throughput, uptime, and cost-efficiency, plus a measurable decline in MTTR through self-serve incident management
You don't like getting technical
You prefer only strategy, UX, or writing great docs over doing whatever it takes to ship great products for customers
You lean toward applied AI over building platform, systems, GPUs, models, and scaling platforms and infrastructure
You're not interested in the foundational, sometimes unglamorous work of making AI systems reliable and scalable at the infrastructure level