Site Reliability Engineer

Evlo AI; San Francisco (CA); On-site; USD 140,000 - 190,000; Full time

Job summary

Evlo AI is seeking a seasoned SRE to own the reliability of large-scale production systems. You will collaborate with backend engineers and platform teams to eliminate toil, lower error budgets, and codify incident response as a repeatable practice.

You will build and scale Kubernetes-based infrastructure on AWS or GCP, design observability tooling and dashboards, and lead blameless postmortems to drive continuous improvement across teams.

Qualifications

• 3–7 years of experience in SRE, DevOps, or infrastructure engineering.
• Strong Kubernetes proficiency for clusters, debugging workloads, and Helm charts.
• Experience with at least one major cloud (AWS, GCP, or Azure) including networking and IAM.
• Observability stacks experience (Prometheus/Grafana, Datadog) and structured incident response.
• Fluency in Go, Python, or Bash for automation and tooling.
• Solid understanding of distributed systems: load balancing, caching, and failure modes.
• Bonus: service mesh, chaos engineering, multi-region architectures.

Responsibilities

• Build, operate, and scale Kubernetes-based infrastructure on AWS or GCP, including upgrades, autoscaling, and node pool optimization.
• Design and maintain observability tooling using Prometheus, Grafana, and OpenTelemetry — dashboards, SLOs, and actionable alerts instead of noise.
• Lead incident response for production outages: run the bridge, root cause analysis, and publish blameless postmortems with tracked follow-ups.
• Automate infrastructure provisioning with Terraform and reduce manual operations through CI/CD pipelines (GitHub Actions, ArgoCD).
• Implement capacity planning and load testing to identify bottlenecks before they become outages.
• Define and enforce reliability standards — SLO/SLI frameworks, error budget policies, and runbooks — across engineering teams.

Skills

SRE experience

Kubernetes proficiency

Cloud platforms

Observability tooling

Incident response

Automation scripting

Education

BS in Computer Science or equivalent

Tools

Terraform

ArgoCD

Prometheus

Grafana

OpenTelemetry

Datadog

Helm

Job description

About The Role

The role owns the reliability of large-scale production systems — Kubernetes clusters, distributed services, and the infrastructure they run on — serving real traffic every minute of every day.

You will work directly with backend engineers and platform teams to eliminate toil, drive down error budgets, and turn incident response into a practiced discipline rather than an emergency.

Key Responsibilities

• Build, operate, and scale Kubernetes-based infrastructure on AWS or GCP, including cluster upgrades, autoscaling policies, and node pool optimization
• Design and maintain observability tooling using Prometheus, Grafana, and OpenTelemetry — dashboards, SLOs, and actionable alerts instead of noise
• Lead incident response for production outages: run the bridge, drive root cause analysis, and publish blameless postmortems with tracked follow-ups
• Automate infrastructure provisioning with Terraform and reduce manual operations through CI/CD pipelines (GitHub Actions, ArgoCD)
• Implement capacity planning and load testing to identify bottlenecks before they become outages
• Define and enforce reliability standards — SLO/SLI frameworks, error budget policies, and runbooks — across engineering teams

What We Are Looking For

• 3–7 years of experience in SRE, DevOps, or infrastructure engineering, including on-call for production systems at scale
• Strong proficiency with Kubernetes: operating clusters, debugging workloads, and writing custom controllers or Helm charts
• Deep experience with at least one major cloud (AWS, GCP, or Azure), including networking, IAM, and cost optimization
• Production experience with observability stacks: Prometheus/Grafana, Datadog, or equivalent, plus structured incident response
• Fluency in at least one of: Go, Python, or Bash for automation and tooling
• Solid grasp of distributed systems fundamentals: load balancing, consensus, caching, backpressure, and failure modes
• Bonus: Experience with service mesh (Istio/Linkerd), chaos engineering, multi-region architectures, or on-prem/hybrid infrastructure. BS in Computer Science or equivalent practical experience.