
Senior Kubernetes Platform Specialist
1 week ago
- Architecting and implementing GitOps-driven Kubernetes cluster lifecycle automation using tools like kubeadm, ClusterAPI, Helm, and Argo CD.
- Developing and managing declarative infrastructure components for GPU stack deployment (e.g., NVIDIA GPU Operator), container runtime configuration (Containerd), networking layers (CNI plugins like Calico, Cilium, etc.),
- Leading automation efforts to enable zero-touch upgrades and certification pipelines for Kubernetes clusters and associated workloads.
- Maintaining Git-backed sources of truth for all platform configurations and integrations.
- Standardizing deployment practices across multi-cluster GPU environments, ensuring scalability, repeatability, and compliance.
- Driving observability, testing, and validation as part of the continuous delivery process (e.g., cluster conformance, GPU health checks).
- Collaborating with infrastructure, security, and SRE teams to ensure seamless handoffs between lower layers (hardware/OS) and the Kubernetes platform.
- Mentoring junior engineers and contributing to the platform automation roadmap.
- 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.
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