About The Role
The role owns the reliability of production infrastructure serving millions of requests per day - Kubernetes clusters, distributed services, and the observability stack that keeps them honest.
You will sit at the intersection of infrastructure engineering and incident response: automating away toil, designing SLOs that actually mean something, and being the person the on-call rotation trusts when things break at 3am.
Key Responsibilities
Design, build, and operate containerized infrastructure on Kubernetes (EKS/GKE), including cluster upgrades, autoscaling policies, and multi-environment rollout strategies
Build and maintain infrastructure-as-code using Terraform and Helm, with CI/CD pipelines in GitHub Actions or GitLab CI for infrastructure changes
Define and enforce SLOs/SLIs with error budgets; build dashboards and alerts in Prometheus, Grafana, and Datadog that page on real problems, not noise
Lead incident response for production outages - triage, mitigation, root cause analysis, and blameless postmortems with actionable follow-through
Automate away recurring operational toil: runbook automation, self-healing infrastructure, and capacity planning scripts
Harden production systems for reliability and security - chaos engineering experiments, disaster recovery drills, and failover testing
Partner with application engineers on service design reviews, ensuring new services ship with proper observability, load testing, and rollback paths
What We Are Looking For
3-7 years of experience in SRE, DevOps, or infrastructure engineering, including significant on-call ownership of production systems
Deep hands-on expertise with Kubernetes in production - operating clusters, debugging workloads, and tuning resource limits and autoscaling behavior
Strong infrastructure-as-code skills with Terraform (or equivalent) and experience managing cloud infrastructure on AWS, GCP, or Azure
Production experience with observability tooling: Prometheus/Grafana, Datadog, or OpenTelemetry, including writing PromQL/queries from scratch
Proficiency in at least one systems language: Go, Python, or Bash, with a track record of building internal tooling
Solid grasp of distributed systems fundamentals: load balancing, caching, DNS, TLS, queuing, and failure modes at scale
Bonus: experience with chaos engineering tools (Litmus, Gremlin), service mesh (Istio/Linkerd), cost optimization (FinOps), or running multi-region active-active architectures