What you'll do:

• Own the reliability and scalability of LLM serving in production
• Build and run deployment, autoscaling, and orchestration on Kubernetes
• Instrument serving with real observability — time-to-first-token, tokens/sec, latency percentiles, error budgets
• Set and defend SLOs; load-test and harden the platform against traffic spikes
• Operate the serving stack (vLLM / Triton / TensorRT-LLM) as a dependable production system

What you'll bring:

• Strong production / SRE / platform-engineering experience running services at scale
• Deep Kubernetes and cloud infrastructure skills
• Observability and reliability discipline (SLOs, monitoring, incident response)
• Comfort operating GPU-backed or ML workloads — a deep ML background is not required
• Solid software engineering fundamentals

Nice to have:

• Experience serving ML/LLM models specifically
• Familiarity with inference frameworks (vLLM, Triton, TensorRT-LLM)
• Performance / load-testing background