Senior DevOps Engineer

Role overview

Responsibilities

• Design and maintain scalable, reproducible infrastructure using infrastructure as code.
• Own CI/CD pipelines for build, testing, security, and production deployment.
• Manage container workloads across Kubernetes and cloud compute environments.
• Build safe rollout, rollback, and recovery strategies.
• Manage cloud networking, API gateways, load balancing, DNS, certificates, and service connectivity.
• Implement identity, secrets management, and least-privilege access.
• Operate production databases with high availability, replication, backup, and recovery.
• Establish infrastructure and application monitoring, alerting, and operational metrics.
• Lead incident response and improve reliability, security, and operational resilience.
• Optimize infrastructure utilization and costs, including high-compute workloads.
• Build reusable platform capabilities that enable engineering teams to operate services independently.

Experience and technical requirements

• 8+ years in DevOps, SRE, infrastructure, or platform engineering.
• Strong hands-on AWS experience across compute, networking, IAM, and storage.
• Strong Terraform or OpenTofu experience, including reusable modules and state management.
• Experience with Ansible or comparable configuration management.
• Strong CI/CD experience, particularly GitHub Actions or equivalent.
• Strong Docker, Kubernetes, and Helm experience; EKS is highly desirable.
• GitOps experience, including ArgoCD or Flux.
• Experience with API gateways, authentication, routing, and rate limiting.
• Production experience with PostgreSQL, RDS/Aurora, and Redis or equivalent technologies.
• Experience with Vault, AWS Secrets Manager, or Parameter Store.
• Strong observability experience with Prometheus, Grafana, CloudWatch, and OpenTelemetry or equivalents.
• Comfort with Linux, Bash, and Python; strong understanding of security, reliability, incident management, and production operations.

Advanced infrastructure

• Production experience operating AI, machine-learning, or other high-compute workloads is highly desirable.
• Relevant areas include GPU infrastructure and workload scheduling, model-serving or inference platforms, autoscaling and capacity management, and performance, utilization, and cost management.
• Relevant technologies include vLLM, Triton, TGI, KServe, Ray Serve, Amazon Bedrock, and SageMaker.

Technical environment