Kubernetes Expert

3 days ago

Chennai, India Microcoreware Full time

Description :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. (ref:hirist.tech)

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