Platform Operations Engineer

RTX•Clovis, CA

3d•$86,800 - $165,200•Remote

About The Position

At RTX, the world's largest aerospace and defense company, 185,000 great minds are united by purpose and inspired to make a difference solving the world’s most complex problems. With our three market leading businesses, world-class operations and investments in research and development, we offer capabilities and opportunity no one else can. Together, we push the boundaries of known science and find new ways to connect and protect our world. Raytheon brings the strength of more than 100 years of experience and renowned engineering expertise to meet the needs of today’s mission and stay ahead of tomorrow’s threat. We deliver solutions that help our nation and allies defend freedoms and deter aggression, creating a safer, more secure world. Join us and help shape the future of aerospace and defense. About the Team ROCKS (Raytheon Orchestration and Container Kubernetes Service) is the Platform Engineering team providing the standard container orchestration foundation for Raytheon's Digital Ecosystem — a modern, Kubernetes-based platform that runs equally well on air-gapped classified information systems and as a shared unclassified service across our networks. ROCKS products are built into Raytheon's multi-SBU architecture standards and support both classified program deployments and a growing portfolio of unclassified development, AI/analytics, and CI/CD workloads. This role sits on the team responsible for the Unclassified Managed Service — the platform supporting over 50 internal programs today, and the proving ground where much of ROCKS' platform and automation innovation gets validated before it moves into classified environments.

Requirements

Typically requires a University degree or equivalent experience and a minimum of 5 years of prior relevant experience or an Advanced Degree in a related field and minimum 3 years experience.
The ability to obtain and maintain a U.S. government issued security clearance is required. U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance.
Experience installing, deploying, monitoring, and supporting Kubernetes clusters on-premises and/or in the cloud — with platforms such as Rancher RKE2, Upstream Kubernetes, OpenShift, or VMware VKS/Tanzu.
Working knowledge of Kubernetes-adjacent tooling: Helm, Ansible, Terraform, Python, and Bash.
Experience with observability and monitoring tooling such as Grafana, Prometheus, Alertmanager, or Loki.

Nice To Haves

Experience deploying new cloud-native platforms in classified and/or unclassified environments.
Experience designing and operating highly available, secure, high-performing Kubernetes clusters at scale.
Experience with VMware, AWS GovCloud, or Azure for Government.
Experience with GitOps and Kubernetes package management (ArgoCD, Packer, Helm, Kustomize).
Experience working in an agile/product-mode team alongside product owners and scrum masters.
Experience with CNCF-ecosystem components: service mesh, service discovery, package management, observability, runtimes, and security.

Responsibilities

Own day-to-day reliability, performance, and operations of Kubernetes clusters supporting active internal customer use cases (including GPU-enabled workloads).
Partner directly with internal program teams to onboard new workloads, forecast capacity, and support scaling as usage grows.
Diagnose and resolve complex Kubernetes issues across the platform, escalating architecture-level problems to the Platform Engineering pillar when needed
Build and improve observability — monitoring, alerting, and dashboards (Prometheus, Grafana) — to drive down MTTD/MTTR and improve service availability.
Work with our internal CI/CD partner team to support GitOps-based deployment (ArgoCD, Helm) for internal customers running their software factories on top of ROCKS-managed infrastructure.
Support infrastructure automation and scaling across bare-metal, VMware, and cloud-adjacent government environments.
Contribute ideas for platform enhancements based on what you see across internal customer usage patterns.