About The Role :

The Senior SRE will be positioned at the intersection of backend engineering, infrastructure operations, and FinOps.

The role is explicitly broader than a traditional DevOps engineer and explicitly more hands-on than a pure architect.

What 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 changes.

We'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.