Kubernetes Expert

Role Overview

We are looking for an experienced Kubernetes with strong expertise in Kubernetes clusters, cloud-native technologies, storage integration, and performance optimisation. The ideal candidate should have hands-on experience in designing, deploying, and managing large-scale Kubernetes environments across on-prem and cloud platforms, along with troubleshooting complex containerised workloads.

Key Responsibilities

• Cluster Management & Deployment
  
• Provision and manage Kubernetes clusters using kubeadm, RKE2, and Cluster API across cloud platforms (AWS, Azure, GCP, OpenStack).
  
• Deploy, scale, and upgrade applications using Kubernetes best practices (rolling updates, probes, HPA, VPA).
  
• Configure node scheduling strategies using taints, tolerations, and affinity rules.
  
• Application Deployment & Troubleshooting
  
• Debug CrashLoopBackOff and pod failures using kubectl logs, events, and resource monitoring.
  
• Troubleshoot networking, persistent volumes, and service exposure issues (ClusterIP, NodePort, LoadBalancer, Ingress).
  
• Debug application routing using APISIX, NGINX ingress, and multi-path routing.
  
• Handle application scaling and high-traffic scenarios using autoscalers.
  
• Storage & Data Management
  
• Integrate Ceph storage with Kubernetes via CSI drivers for block and filesystem provisioning.
  
• Troubleshoot PersistentVolume (PV) and PersistentVolumeClaim (PVC) issues.
  
• Observability & Performance
  
• Deploy and configure monitoring solutions such as Prometheus and Metrics Server.
  
• Benchmark cluster and workload performance (CPU, memory, networking).
  
• Enable log collection and analysis for multi-container pods.
  
• Security & Networking
  
• Manage authentication and RBAC policies within Kubernetes.
  
• Configure isolation for virtual Kubernetes clusters (vcluster).
  
• Handle registry authentication (AWS ECR, private registries) using image pull secrets.
  
• Specialized Workloads
  
• Deploy and manage GPU workloads using NVIDIA GPU Operator.
  
• Enable GPU scheduling and resource allocation for AI/ML workloads.
  
• Operations & Maintenance
  
• Troubleshoot faulty nodes (on-prem / cloud) including CPU, memory, disk, and kubelet health.
  
• Work on service routing, ingress configurations, and debugging cloud load balancer/firewall issues.
  
• Perform rolling upgrades and ensure zero-downtime deployments.

Required Skills

• Strong expertise in Kubernetes administration and cloud-native deployments.
  
• Hands-on experience with kubeadm, RKE2, Cluster API, and Terraform for cluster provisioning.
  
• Knowledge of storage integration with Ceph and CSI drivers.
  
• Experience with monitoring and observability tools (Prometheus, Grafana, Metrics Server).
  
• Strong debugging skills for pod crashes, networking issues, and persistent storage problems.
  
• Knowledge of NGINX ingress, APISIX, and traffic routing.
  
• Understanding of RBAC, security groups, and IAM policies in Kubernetes & cloud.
  
• Experience with GPU workloads in Kubernetes.
  
• Familiarity with CI/CD pipelines for Kubernetes deployments is a plus.

Preferred Qualifications

• 4+ years of hands-on experience in Kubernetes roles.
  
• Experience in both managed (EKS, AKS, GKE) and on-prem Kubernetes clusters.
  
• Strong scripting skills (Bash, Python, Go – preferred).
  
• Prior experience with infrastructure-as-code tools like Terraform, Helm, and Ansible.
  
• Exposure to multi-cluster and multi-tenant environments.