K8S Lifecycle Automation Engineer (RARR Job 5420)

Position Overview

We are seeking a Senior Kubernetes Platform Engineer to design and implement the ZeroTouch Build, Upgrade, and Certification pipeline for our on-premises GPU cloud platform. This role will focus on automating the Kubernetes layer and its dependencies (e.g., GPU drivers, networking, runtime) using 100% GitOps workflows. You will collaborate across teams to deliver a fully declarative, scalable, and reproducible infrastructure stack — from hardware to Kubernetes and platform services.

Key Responsibilities

• Architect and implement GitOps-driven Kubernetes cluster lifecycle automation using tools like kubeadm, ClusterAPI, Helm, and Argo CD.

• Develop and manage declarative infrastructure components for:

  • GPU stack deployment (e.g., NVIDIA GPU Operator)

  • Container runtime configuration (Containerd)

  • Networking layers (CNI plugins such as Calico, Cilium, etc.)

• Lead automation initiatives to enable zero-touch upgrades and certification pipelines for Kubernetes clusters and workloads.

• Maintain Git-backed sources of truth for all platform configurations and integrations.

• Standardize deployment practices for multi-cluster GPU environments ensuring scalability, repeatability, and compliance.

• Integrate observability, testing, and validation into continuous delivery (e.g., cluster conformance, GPU health checks).

• Collaborate with infrastructure, security, and SRE teams to ensure smooth handoffs between hardware/OS and Kubernetes platform layers.

• Mentor junior engineers and shape the platform automation roadmap.

Required Skills & Experience

• 10+ years of hands-on infrastructure engineering experience with strong Kubernetes focus.

• Core expertise in: Kubernetes API, Helm templating, Argo CD, GitOps integration, Go/Python scripting, Containerd.

• Deep knowledge of:

  • Kubernetes cluster management (kubeadm, ClusterAPI)

  • Argo CD for GitOps-based delivery

  • Helm for application and cluster add-on packaging

  • Containerd as a container runtime in GPU workloads

• Experience deploying & managing NVIDIA GPU Operator or equivalent in production.

• Strong understanding of CNI plugin ecosystems, network policies, and multi-tenant networking.

• Proven track record with Infrastructure-as-Code using Git-based workflows.

• Experience building Kubernetes clusters in on-premises environments (vs managed cloud services).

• Solid scripting/automation skills (Bash, Python, Go).

• Familiarity with Linux internals, systemd, and OS-level tuning for container workloads.

Preferred / Bonus Skills

• Experience developing custom controllers/operators or Kubernetes API extensions.

• Contributions to Kubernetes or CNCF projects.

• Exposure to service meshes, ingress controllers, or workload identity providers.