GPU Cloud Platform Engineer

2 weeks ago

Varanasi, Uttar Pradesh, India beBeeKubernetes Full time ₹ 15,00,000 - ₹ 25,00,000

Job Overview

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We are seeking a highly skilled and experienced Senior Kubernetes Platform Engineer to join our team. As a key member of our infrastructure engineering team, you will be responsible for designing and implementing the Zero-Touch Build, Upgrade, and Certification pipeline for our on-premises GPU cloud platform.

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The ideal candidate will have a strong background in Kubernetes cluster management, GitOps automation, and container runtime configuration. You will work closely with our teams to deliver a fully declarative, scalable, and reproducible infrastructure stack from hardware to Kubernetes and platform services.

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Key Responsibilities

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  • Design 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), and 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">
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Required Skills & Qualifications

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  • 8+ 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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