Senior AWS DevOps Engineer

Job title:: Senior AWS DevOps Engineer (alternate: Senior Cloud / Platform Engineer) About the role We are hiring a Senior DevOps Engineer to own and evolve our AWS cloud platform, with a strong focus on infrastructure as code, secure multi-account patterns, reliable delivery, and production-grade Amazon EKS operations. This person will shape the DevOps roadmap across standards, tooling, automation, operational excellence, and cost-conscious platform practices. Amazon EKS is central to how we run workloads. We need someone who has built and owned Kubernetes on AWS end-to-end, including cluster lifecycle, networking, security, observability, capacity, reliability, and incident response—not someone whose experience is limited to deploying applications to a cluster managed by others. The role also includes leading practical AI adoption for infrastructure and platform work, such as AI-assisted authoring and review for IaC and automation, stronger runbooks, incident workflows, and evaluation of tools that improve speed without weakening security, compliance, or change control. What you will do DevOps strategy: Define and socialize priorities across security, reliability, cost, and delivery velocity. Align teams on AWS Well-Architected practices, tagging, guardrails, and repeatable patterns for networking, identity, secrets, and data. Infrastructure as code: Design, review, and implement changes using Terraform and Terragrunt, with clear module boundaries, environment-specific configuration, and safe promotion across dev, non-prod, and production. EKS ownership: Build, operate, and own the Kubernetes platform on AWS, including cluster lifecycle, upgrades, node capacity, networking, security, add-ons, cost tuning, reliability, workload standards, namespaces, safe rollouts, and escalation support for cluster-level incidents. Broader AWS platform: Operate and improve adjacent services such as RDS/Aurora, DynamoDB, object storage and CDN, KMS, Secrets Manager, SNS, Lambda, EventBridge, CI/CD, IAM, VPC, and multi-tenant or multi-namespace patterns where applicable. Release engineering: Partner with development teams on release processes, deployment strategies, change management, rollbacks, and post-release verification in regulated or high-stakes environments. Production support: Participate in on-call or escalation rotation as defined by the team; troubleshoot incidents, drive root-cause analysis, and implement preventive fixes through runbooks, dashboards, alarms, and automation. Observability and operations: Improve monitoring, logging, tracing, and alerting; tune thresholds; reduce noise; and document operational procedures. Collaboration and governance: Work with security, architecture, and engineering leads to implement least-privilege access, encryption, backup/DR posture, and audit-friendly operations, including expectations for AI-assisted workflows. AI adoption for infrastructure: Drive a pragmatic AI strategy for IaC, pipeline changes, documentation, runbooks, incident triage, and operational workflows. Establish review gates, testing expectations, drift detection, and guardrails so AI tooling fits regulated or high-stakes environments. What we are looking for Experience: 8+ years in DevOps / SRE roles, including 4+ years focused on AWS in production. Infrastructure as code: Strong command of Terraform and modular, environment-driven layouts. Experience with Terragrunt or similar composition patterns is a plus. Amazon EKS: Deep, mandatory experience building and owning Kubernetes on AWS, including cluster design and lifecycle, upgrades, patching, networking, identity and security, observability, capacity, performance, and production troubleshooting. Surface-level "kubectl-only" experience is not sufficient. CI/CD and change control: Solid grasp of artifact promotion, secrets injection, rollback planning, and safe change practices across multi-environment pipelines. Production operations: Experience with incident triage, communication, root-cause analysis, and durable remediation. AI for DevOps: Demonstrated interest or experience applying AI to platform work, such as AI-assisted IaC review, internal tooling, operational documentation, or incident workflows, with sound judgment around verification, risk, and production limits. Influence and standards: Ability to influence without authority through written standards, design reviews, and roadmap proposals that engineering teams can adopt. Communication: Excellent communication skills and comfort working with distributed teams and stakeholders outside pure engineering.