Site Reliability Engineer

Evlo AI; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Evlo AI is looking for a Platform SRE/DevOps engineer to keep a multi-region production platform reliable, observable, and fast. You will own SLOs, automate toil away, and treat incidents as engineering problems rather than heroics.

You will join a platform team managing Kubernetes-based infrastructure, CI/CD pipelines, and observability tooling used by product teams, directly impacting velocity and user experience.

Qualifications

• BS in CS or equivalent practical experience.
• 3–7 years of experience in SRE/DevOps or backend infra with ownership of prod systems.
• Strong scripting/programming in Go, Python or Bash; ability to build internal tooling.

Responsibilities

• Design, implement, and maintain SLOs, SLIs, and error budgets for production services.
• Build and operate Kubernetes infrastructure across regions with autoscaling and capacity planning.
• Develop IaC using Terraform and Helm with automated drift detection.
• Lead incident response: triage, mitigation, root-cause analysis, blameless postmortems.
• Instrument services with Prometheus, Grafana, and OpenTelemetry; build dashboards and paging policies.
• Automate manual toil via tooling, runbooks, and self-service platforms.
• Improve CI/CD pipelines for faster, safer deployments with canary rolls and automatic rollbacks.

Skills

Go

Python

Bash

Cloud platforms

Kubernetes ops

Observability

Distributed systems

Linux fundamentals

Education

BS in Computer Science or equivalent

Tools

Kubernetes

Terraform

Helm

Prometheus

Grafana

Datadog

OpenTelemetry

CI/CD tooling

Job description

About The Role

The role sits at the intersection of infrastructure engineering and software engineering, keeping a production platform serving millions of requests per day reliable, observable, and fast. This means owning SLOs, eliminating toil through automation, and treating incidents as engineering problems rather than heroics.

About The Role

The role sits at the intersection of infrastructure engineering and software engineering, keeping a production platform serving millions of requests per day reliable, observable, and fast. This means owning SLOs, eliminating toil through automation, and treating incidents as engineering problems rather than heroics.
You will join a platform team responsible for Kubernetes-based infrastructure, CI/CD pipelines, and observability tooling used by product engineering teams across the company. The work is high-leverage: every reliability improvement directly impacts engineering velocity and customer experience.

Key Responsibilities

• Design, implement, and maintain SLOs, SLIs, and error budgets for critical production services, driving prioritization of reliability work across teams
• Build and operate Kubernetes infrastructure (EKS/GKE) including cluster upgrades, autoscaling policies, and capacity planning for multi-region deployments
• Develop infrastructure-as-code using Terraform and Helm, with peer-reviewed, tested modules and automated drift detection
• Lead incident response for production outages: triage, mitigation, root-cause analysis, and blameless postmortems with tracked follow-up items
• Instrument services with Prometheus, Grafana, and distributed tracing (OpenTelemetry), building actionable dashboards and paging policies that cut alert noise
• Automate away manual operational work through tooling, runbooks, and self-service platforms for engineering teams
• Improve CI/CD pipelines for faster, safer deploys — progressive rollouts, canary analysis, and automated rollback

What We Are Looking For

• 3–7 years of experience in SRE, DevOps, or backend infrastructure engineering, with ownership of production systems at meaningful scale
• Strong programming or scripting skills in Go, Python, or Bash; comfort writing internal tooling, not just configuring systems
• Hands‑on expertise with Kubernetes in production: operating clusters, debugging workloads, and managing stateful services
• Deep familiarity with observability stacks (Prometheus, Grafana, Datadog, or similar) and modern incident management practices
• Experience with a major cloud provider (AWS, GCP, or Azure), including networking, IAM, and cost awareness
• BS in Computer Science or equivalent practical experience; strong systems fundamentals (Linux internals, networking, distributed systems)