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.