What are the responsibilities and job description for the Site Reliability Engineer position at Evlo AI?
About The Role
The role owns the reliability of production systems serving real traffic at scale — Kubernetes clusters, distributed services, and the CI/CD pipelines that ship them. This is hands-on infrastructure work: building observability from scratch where it doesn't exist, automating away toil, and cutting MTTR when things break at 3 AM.
You will sit between engineering and operations, working directly with software engineers to make services faster, more resilient, and easier to operate. The impact is measurable: uptime, latency, deploy velocity, and incident-free days.
Key Responsibilities
The role owns the reliability of production systems serving real traffic at scale — Kubernetes clusters, distributed services, and the CI/CD pipelines that ship them. This is hands-on infrastructure work: building observability from scratch where it doesn't exist, automating away toil, and cutting MTTR when things break at 3 AM.
You will sit between engineering and operations, working directly with software engineers to make services faster, more resilient, and easier to operate. The impact is measurable: uptime, latency, deploy velocity, and incident-free days.
Key Responsibilities
- Build and maintain Kubernetes-based infrastructure on AWS/GCP, including cluster provisioning, autoscaling policies, and cost optimization
- Design and implement observability stacks using Prometheus, Grafana, and OpenTelemetry — dashboards, SLOs, and actionable alerting, not noise
- Lead incident response for production outages: triage, mitigation, root cause analysis, and blameless postmortems with tracked follow-through
- Automate infrastructure as code using Terraform and Ansible, enforcing review and drift-detection workflows across environments
- Improve CI/CD pipelines (GitHub Actions, ArgoCD) to enable safe, frequent deployments with progressive rollouts and automated rollback
- Define and drive SLO/SLA frameworks with product teams, balancing reliability targets against feature velocity
- Capacity plan and load test critical services, identifying bottlenecks before they become incidents
- 3–7 years of experience in SRE, DevOps, or infrastructure engineering, including on-call ownership of production systems
- Deep hands-on experience with Kubernetes in production — operating clusters, debugging workloads, and writing operators or Helm charts
- Strong scripting and automation skills in Python, Go, or Bash
- Production experience with at least one major cloud (AWS, GCP, Azure) and infrastructure-as-code tooling (Terraform)
- Solid grasp of distributed systems fundamentals: networking, DNS, load balancing, caching, and failure modes
- Experience with observability tooling (Prometheus/Grafana, Datadog, or similar) and incident management best practices
- Bachelor's degree in Computer Science or equivalent practical experience. Bonus: experience with service meshes (Istio/Linkerd), chaos engineering, multi-region architectures, or Rust/Go tooling development