Expert GPU Cloud Architect

1 week ago

Thoothukudi, Tamil Nadu, India beBeeKubernetes Full time ₹ 1,50,00,000 - ₹ 2,50,00,000

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Job Summary:

Senior Kubernetes Platform Engineer (Zero-Touch GPU Cloud – GitOps Automation)

We are looking for a Senior Kubernetes Platform Engineer 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 using 100% GitOps workflows. You will work 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 container runtime configuration networking layers 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 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 Containerd 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.
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