Kubernetes +Terraform +Python

We are looking for a Senior Kubernetes Platform Engineer with 10+ years of infrastructure experience to design and implement the Zero-Touch Build, Upgrade, and Certification pipeline for our on-premises GPU cloud platform. This role focuses on automating the Kubernetes layer and its dependencies (e.g., GPU drivers, networking, runtime) using 100% GitOps workflows. You will work across teams to deliver a fully declarative, scalable, and reproducible infrastructure stackfrom 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 like Calico, Cilium, etc.)
• Lead automation efforts to enable zero-touch upgrades and certification pipelines for Kubernetes clusters and associated workloads.
• Maintain Git-backed sources of truth for all platform configurations and integrations.
• Standardize deployment practices across multi-cluster GPU environments, ensuring scalability, repeatability, and compliance.
• Drive observability, testing, and validation as part of the continuous delivery process (e.g., cluster conformance, GPU health checks).
• Collaborate with infrastructure, security, and SRE teams to ensure seamless handoffs between lower layers (hardware/OS) and the Kubernetes platform.
• Mentor junior engineers and contribute to the platform automation roadmap.

Required Skills & Experience

• 10+ years of hands-on experience in infrastructure engineering, with a strong focus on Kubernetes-based environments.
• Primary key skills required are Kubernetes API, Helm templating, Argo CD GitOps integration, Go/Python scripting, Containerd
• Deep knowledge and hands-on experience with:
• Kubernetes cluster management (kubeadm, ClusterAPI)
• Argo CD for GitOps-based delivery
• Helm for application and cluster add-on packaging
• Container as a container runtime and its integration in GPU workloads
• Experience deploying and operating the NVIDIA GPU Operator or equivalent in production environments.
• Solid understanding of CNI plugin ecosystems, network policies, and multi-tenant networking in Kubernetes.
• Strong GitOps mindset with experience managing infrastructure as code through Git-based workflows.
• Experience building Kubernetes clusters in on-prem environments (vs. managed cloud services).
• Proven ability to scale and manage multi-cluster, GPU-accelerated workloads with high availability and security.
• Solid scripting and automation skills (Bash, Python, or Go).
• Familiarity with Linux internals, systemd, and OS-level tuning for container workloads.

Bonus:

Experience with custom controllers, operators, or Kubernetes API extensions

Contributions to Kubernetes or CNCF projects

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