Site Reliability Engineer

Evlo AI; Dallas (TX); On-site; USD 130,000 - 190,000; Full time

Job summary

Evlo AI is seeking an experienced SRE/DevOps engineer to own the reliability of large-scale production systems. You will manage Kubernetes clusters, CI/CD pipelines, and service mesh observability to ensure uptime and performance, handling millions of requests per minute.

The role emphasizes blameless postmortems, automation over toil, and collaboration with product teams to translate reliability targets into engineering priorities. A strong background in IaC and cloud services is required.

Qualifications

• 3-7 years of experience in SRE, DevOps, or backend infrastructure engineering.
• Hands-on Kubernetes in production and cluster operations.
• Infrastructure-as-code with Terraform; familiarity with AWS or GCP core services.
• Proficiency in Go, Python, or Bash for tooling and automation.
• Understanding of distributed systems failure modes and resilience design.
• Experience with incident response and postmortems; strong written communication.
• Bonus: Istio/Linkerd service mesh, chaos engineering, multi-cloud.

Responsibilities

• Build, operate, and scale Kubernetes infrastructure (EKS/GKE) across multi-region environments.
• Define and enforce SLOs and error budgets with product teams.
• Design observability with metrics, logs, and traces using Prometheus, Grafana, and OpenTelemetry.

Skills

Kubernetes production

Go

Python

Bash

On-call ownership

Distributed systems

Education

BS/MS in CS

Tools

Terraform

AWS

GCP

OpenTelemetry

Job description

About The Role

The role owns the reliability of large-scale, distributed production systems — Kubernetes clusters, service meshes, and the CI/CD pipelines that ship code to them hundreds of times a day.

About The Role

The role owns the reliability of large-scale, distributed production systems — Kubernetes clusters, service meshes, and the CI/CD pipelines that ship code to them hundreds of times a day. This is a hands-on SRE position on a team that treats uptime as an engineering problem: error budgets, SLOs, and automation over toil. The work directly protects revenue and user experience across systems handling millions of requests per minute.

Key Responsibilities

• Build, operate, and scale Kubernetes infrastructure (EKS/GKE) across multi-region environments, including cluster upgrades, autoscaling policies, and workload placement
• Define and enforce SLOs and error budgets with product teams, translating reliability targets into concrete engineering priorities
• Design observability from the ground up: metrics, logs, and traces using Prometheus, Grafana, and OpenTelemetry, with actionable alerting that pages humans only when it should
• Automate infrastructure provisioning with Terraform and GitOps workflows (ArgoCD or Flux), eliminating manual configuration and drift
• Lead blameless postmortems for production incidents, drive root-cause fixes, and reduce mean time to recovery through runbooks and tooling
• Improve CI/CD pipelines for safe, fast deploys — progressive rollouts, canary analysis, and automated rollback via Spinnaker or Argo Rollouts
• Capacity plan and performance-tune core infrastructure: databases, message queues (Kafka), and load balancers under real production traffic

What We Are Looking For

• 3-7 years of experience in SRE, DevOps, or backend infrastructure engineering, including on-call ownership of production systems at scale
• Deep hands-on expertise with Kubernetes in production — operating clusters, debugging workloads, and writing custom operators or controllers is a plus
• Strong infrastructure-as-code skills with Terraform; comfort with AWS or GCP core services (compute, networking, IAM, storage)
• Proficiency in Go, Python, or Bash for building internal tooling, automation, and observability integrations
• Solid understanding of distributed systems failure modes: consensus, retries, backpressure, cascading failures, and tail latency
• Experience running incident response and postmortem processes; strong written communication under pressure
• Bonus: experience with service mesh (Istio/Linkerd), chaos engineering practices, eBPF-based observability, or multi-cloud architectures; BS/MS in Computer Science or equivalent practical experience