Cloud Infrastructure Engineer

23 hours ago

Thiruvananthapuram, Kerala, India beBeeKubernetes Full time ₹ 2,50,00,000 - ₹ 3,50,00,000
About the Role

We're seeking a seasoned expert to spearhead Kubernetes cluster automation and infrastructure engineering for our on-premises GPU cloud platform.

  • Key Responsibilities:
  • Design and implement GitOps-driven Kubernetes cluster lifecycle automation using kubeadm, ClusterAPI, Helm, and Argo CD.
  • Develop declarative infrastructure components for GPU stack deployment (e.g., NVIDIA GPU Operator), container runtime configuration (Containerd), networking layers (CNI plugins like Calico, Cilium, etc.), and 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.
Requirements
  • 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, 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.

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