Cloud Infrastructure Architect

12 hours ago

Kolkata, West Bengal, India beBeeKubernetes Full time ₹ 20,00,000 - ₹ 30,00,000
Job Title

Senior Kubernetes Engineer of Cloud Infrastructure

We are seeking an experienced Senior Kubernetes Engineer with a strong background in cloud infrastructure to lead the design and implementation of our on-premises GPU cloud platform.

This role involves architecting and implementing GitOps-driven Kubernetes cluster lifecycle automation using tools like kubeadm, ClusterAPI, Helm, and Argo CD.

Key Responsibilities:

  • Design and implement declarative infrastructure components for GPU stack deployment, container runtime configuration, and 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 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, Argo CD, Helm, Containerd, and NVIDIA GPU Operator.
  • 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.
  • 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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