Level AI - Senior Site Reliability Engineer - DevOps in Noida, भारत

About The Role :The Senior SRE will be positioned at the intersection of backend engineering, infrastructure operations, and https://jobeax.com/link/ZPklSqJSxM8eppJu role is explicitly broader than a traditional DevOps engineer and explicitly more hands-on than a pure https://jobeax.com/link/ipHx75t9azGLbRbV You'll Be Liable For :1. Infrastructure cost efficiency and FinOps :- Own the continued reduction of Kubernetes overprovisioning, drive right-sizing programs, and maintain the cost telemetry that backend teams use to make decisions.2. GPU throughput optimization :- Run a structured experimentation program on on-premise GPU clusters, partnering with AI service owners.- Lead by the Engineering leadership, with this role providing the experimental bandwidth.3. Backend enablement, not ownership absorption :- Build the tooling, dashboards, and processes that let backend teams from other groups own their own cost and reliability budgets.- The deliverable is leverage, not headcount-shaped work.4. Reliability instrumentation :- As the infra team owns most of the instrumentation across new and offline flows, this role takes a central seat in making sure that surface area is captured properly for both cost-at-scale and reliability.5. Selective security workstreams :- Take on a defined slice of the active security work so that senior DevOps engineers are not the single point of execution for security-adjacent platform https://jobeax.com/link/vGSK283loejhDQBS'll love to explore more about you if you have :- This role explicitly requires 4 - 5 years of hands-on systems experience.- We are not looking for someone who will lean entirely on AI tooling to discover what to do; we are looking for someone who already knows what to ask, and can use AI tooling as a force multiplier on top of that judgement.- Backend engineering depth: production experience in Python, Go/Rust, comfortable owning services end to end, able to read and reason about backend code across teams.- Kubernetes at scale: scheduler behavior, resource requests/limits, HPA/VPA, node pool design, cost-aware autoscaling (Cast AI, Karpenter, or equivalent).- Cloud and on-premise infrastructure: GCP fluency, IaC (Terraform), CI/CD, and comfort operating in hybrid setups including on-prem GPU clusters.- GPU workload understanding: familiarity with throughput profiling, batching, KV-cache behavior, inference server tuning, and GPU utilization metrics.- Observability and reliability: metrics, traces, logs, SLOs, and the discipline to instrument systems properly rather than reactively.- FinOps mindset: demonstrated history of converting infrastructure choices into measurable cost outcomes.- Security baseline: able to take on platform-security workstreams without requiring constant handoff to the DevOps team. (ref:hirist.tech)