Senior DevOps Engineer (AWS)

**Company Description** PivotPointTech Talent Solutions specializes in IT consulting and technology recruitment, empowering businesses with top\-tier professionals and cutting\-edge digital solutions. We focus on digital strategy, transformation, AI integration, user experience design, and product development, enabling businesses to thrive in today’s fast\-paced digital landscape. Our team of experts provides hands\-on support, ensuring seamless integration and exceptional results. As a trusted partner, we strive to help companies achieve their goals through tailored solutions and strategic insights. **About the role** We are hiring a senior DevOps engineer to own and evolve our cloud platform on AWS, grounded in infrastructure as code, secure multi\-account patterns, and reliable delivery. You will shape the DevOps roadmap (standards, tooling, automation, and operational excellence), support application releases, and provide production support for critical workloads. Amazon EKS is central to how we run workloads—we need someone with deep, production\-grade EKS expertise who has built and owned Kubernetes on AWS end\-to\-end, not only deployed apps to a cluster someone else runs. You will also lead how we adopt AI for infrastructure and platform work—not as a buzzword, but as a practical force multiplier: safe use of AI\-assisted authoring and review for IaC and automation, clearer runbooks and incident workflows, and evaluation of tools and patterns that improve speed without weakening security, compliance, or change control. This role suits someone who combines deep AWS practice with leadership: you can define “how we build and run” while still being hands\-on in pipelines, clusters, and incidents. **What you will do** * **Roadmap \& standards:** Define and socialize DevOps priorities (security, reliability, cost, velocity). Align teams on AWS Well\-Architected practices, tagging, guardrails, and repeatable patterns for networking, identity, secrets, and data. * **AI adoption for infra \& platform:** Drive a pragmatic AI strategy for the team—e.g. standards for AI\-assisted IaC and pipeline changes (review gates, testing, drift detection), documentation and runbook quality, incident summarization and triage workflows where appropriate, and guardrails so AI tooling fits regulated or high\-stakes environments. Stay current on vendor and open\-source options; pilot, measure, and roll out what actually reduces toil. * **Infrastructure as code:** Design, review, and implement changes using Terraform and Terragrunt, with clear module boundaries, environment\-specific config, and safe promotion across dev → non\-prod → production. * **EKS (critical):** Build, operate, and own the Kubernetes platform on AWS—cluster lifecycle (creation, upgrades, patching), node groups / capacity, networking (CNI, service mesh or ingress as used), security (RBAC, admission controls, pod security, secrets and IRSA), add\-ons, and cost/reliability tuning. Partner with app teams on standards for workloads, namespaces, and safe rollouts; be the escalation point for cluster\-level incidents. * **Broader AWS platform:** Operate and improve adjacent services—e.g. RDS/Aurora, DynamoDB, object storage and CDN, KMS, Secrets Manager, SNS (alerting), Lambda, EventBridge, and CI/CD (CodePipeline / CodeBuild, connections to source control)—plus 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 (e.g. healthcare\-adjacent workloads). * **Production support:** Participate in on\-call or escalation rotation as defined by the team; troubleshoot incidents, drive root\-cause analysis, and implement preventive fixes (runbooks, dashboards, alarms, automation). * **Observability \& operations:** Improve monitoring, logging, tracing, and alerting; tune thresholds; reduce noise; document operational procedures. * **Collaboration:** Work with security, architecture, and engineering leads to implement least\-privilege access, encryption, backup/DR posture, and audit\-friendly operations—including how AI\-assisted workflows meet security and audit expectations. **Qualifications** * 6\+ years in software / systems / DevOps / SRE roles, including 4\+ years focused on AWS in production. * Strong command of infrastructure as code (Terraform) and modular, environment\-driven layouts (experience with Terragrunt or similar composition patterns is a plus). * Deep, mandatory expertise in Amazon EKS: You have prior experience building and owning Kubernetes on AWS—not only deploying applications to a shared cluster. We expect fluency across the stack: cluster design and lifecycle, upgrades and patching, networking * (VPC/CNI, DNS, ingress), identity and security (RBAC, IRSA, secrets, guardrails), observability, capacity and performance, and production troubleshooting. Surface\-level or “I’ve used kubectl” experience is not sufficient. * Solid grasp of CI/CD, artifact promotion, secrets injection, and safe change practices in multi\-environment pipelines. * Experience with production incidents: triage, communication, RCAs, and durable remediation. * Demonstrated interest or experience in applying AI to DevOps/platform work (e.g. AI\-assisted coding and review workflows for IaC, internal tooling, or operational documentation)—with judgment about limits, verification, and risk in production systems. * Ability to influence without authority: written standards, design reviews, and roadmap proposals that engineering teams actually adopt. Excellent communication skills; comfortable working with distributed teams and stakeholders outside pure engineering. **Preferred** * AWS certifications (e.g. Solutions Architect Professional, DevOps Engineer) or equivalent demonstrated depth. * Kubernetes certifications (e.g. CKA, CKS) or equivalent evidence of advanced Kubernetes/EKS depth. * Experience with Helm / Helmfile, policy\-as\-code, or cluster baseline tooling. * Familiarity with PostgreSQL/RDS, multi\-tenant data patterns, or regulated\-industry constraints. * Experience shaping SLOs, error budgets, or platform KPIs. * Exposure to cost optimization (rightsizing, scheduling non\-prod, storage lifecycle) and FinOps collaboration. * Hands\-on experimentation with AI coding assistants, internal LLM or RAG patterns for ops knowledge, or evaluating vendor tools for the platform team.