What are the responsibilities and job description for the Site Reliability Engineer position at Evlo AI?
About The Role
The role is for an engineer who treats reliability as a product: someone who instruments systems, defines measurable SLOs, and eliminates whole classes of incidents rather than firefighting the same one twice.
You will join a platform reliability team responsible for large-scale, distributed production infrastructure - Kubernetes clusters serving hundreds of microservices, high-throughput data pipelines, and uptime targets measured in four nines.
Key Responsibilities
The role is for an engineer who treats reliability as a product: someone who instruments systems, defines measurable SLOs, and eliminates whole classes of incidents rather than firefighting the same one twice.
You will join a platform reliability team responsible for large-scale, distributed production infrastructure - Kubernetes clusters serving hundreds of microservices, high-throughput data pipelines, and uptime targets measured in four nines.
Key Responsibilities
- Design, build, and maintain production infrastructure on Kubernetes and AWS/GCP using Terraform, with infrastructure-as-code as the default - no hand-configured environments
- Build and evolve observability tooling (Prometheus, Grafana, OpenTelemetry) so every service ships actionable metrics, logs, and traces from day one
- Define, implement, and enforce SLOs and error budgets; drive reliability discussions with product teams using real latency and error data
- Lead blameless postmortems and drive remediation work to completion, reducing mean time to resolution across the fleet
- Automate incident response - runbooks, self-healing automation, and alerting tuning that keeps pages meaningful and on-call sustainable
- Engineer CI/CD deployment pipelines (GitHub Actions or ArgoCD) enabling safe, frequent releases with progressive rollouts and automated rollback
- Participate in a 24/7 on-call rotation, owning live incident response for customer-facing production systems
- 3–7 years of experience operating production infrastructure at scale, ideally in SRE, DevOps, or platform engineering roles
- Deep hands-on experience with Kubernetes - cluster operations, autoscaling, resource tuning, and troubleshooting under pressure
- Strong scripting and automation skills in Go, Python, or Bash; comfort reading and modifying services in other languages
- Production experience with at least one major cloud (AWS, GCP, or Azure) and Terraform for multi-environment infrastructure
- Fluency in modern observability: Prometheus, Grafana, distributed tracing, and designing alerts that people actually trust
- BS in Computer Science, Engineering, or equivalent practical experience building and running distributed systems
- Bonus: Experience with service mesh (Istio/Linkerd), chaos engineering practices, multi-region failover architecture, or writing production Go services