Kubernetes Engineer

Overview

Multicoreware

Website: Visit Website

Business Type: Startup

Company Type: Product & Service

Business Model: B2B

Funding Stage: Pre-seed

Industry: OTT, Broadcast

Salary Range: ₹ 12-35 Lacs PA

Job Description

Job Description: Kubernetes Engineer

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.
  
  

• Debug CrashLoopBackOff and pod failures using kubectl logs, events, and resource monitoring. o 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.
  
  

• Integrate Ceph storage with Kubernetes via CSI drivers for block and filesystem provisioning.
• Troubleshoot PersistentVolume (PV) and PersistentVolumeClaim (PVC) issues.
  
  

• 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.
  
  

• 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.
  
  

• Deploy and manage GPU workloads using NVIDIA GPU Operator.
• Enable GPU scheduling and resource allocation for AI/ML workloads.
  
  

• 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.
  
  

• 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.
  
  

• 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.