GPU Kubernetes Cluster Engineer (Junior) WFH

Read everything carefully. The requirements and screening questions are critical and if not answered correctly and satisfactorily will result in auto-rejection and waste of your time.

• Work from Home.
• This is a full-time role. If you plan to do 2 or more jobs at the same time or want to do this part-time, that won't work for us. In that case please do not apply as it will get auto-rejected
• Note - this job requires working late night India time until 4AM to overlap with USA working times. Do not apply if this timing doesn't work
• Salary depends on experience and current verifiable (paychecks) compensation.
• Junior candidates with 2 years experience are suitable GPU Cluster & Kubernetes Engineer (Linux Infrastructure) About Qubrid AI Qubrid AI is building a full-stack AI infrastructure platform that combines GPU cloud, inference APIs, AI orchestration software, and enterprise AI infrastructure. Our platform powers AI workloads across cloud, hybrid, and on-prem environments using state-of-the-art NVIDIA technologies and open-source AI frameworks. We are seeking a hands-on

GPU Cluster & Kubernetes Engineer with deep Linux and Kubernetes expertise to deploy, manage, and optimize GPU clusters used for AI training and large-scale inference workloads. Role Overview As a GPU Cluster & Kubernetes Engineer, you will be responsible for building and operating highly available Linux-based GPU clusters. You will work across Kubernetes, container platforms, networking, storage, and NVIDIA technologies to deliver reliable, scalable infrastructure for AI workloads. The ideal candidate enjoys troubleshooting complex distributed systems and automating infrastructure at scale. Responsibilities

• Deploy, configure, and maintain production Kubernetes clusters for AI and GPU workloads.
• Build and operate Linux-based GPU clusters supporting training and inference environments.
• Install and manage NVIDIA GPU Operator, drivers, CUDA, and container runtimes.
• Configure Kubernetes scheduling, namespaces, quotas, node pools, taints, tolerations, and affinity rules.
• Deploy and manage GPU workloads using Helm, Operators, and GitOps methodologies.
• Configure high availability and cluster upgrades with minimal downtime.
• Troubleshoot Kubernetes, Linux, networking, and storage issues.
• Configure and manage container runtimes including containerd and Docker.
• Implement cluster monitoring, logging, and alerting.
• Automate provisioning and operations using Ansible, Bash, and Python.
• Manage persistent storage solutions and distributed file systems.
• Collaborate with GPU engineers and AI platform teams to optimize infrastructure for inference and training.
• Participate in customer deployments and provide operational support. Required Qualifications
• 4+ years of Linux systems administration experience.
• 3+ years of hands-on Kubernetes administration experience.
• Strong expertise with:
• Kubernetes
• Kubeadm
• Containerd
• Helm
• Docker
• Linux (Ubuntu, Rocky Linux, RHEL)
• Experience deploying and managing multi-node clusters.
• Experience with NVIDIA GPU Operator and GPU-enabled Kubernetes environments.
• Solid understanding of:
• Networking fundamentals
• DNS
• Load balancing
• Ingress controllers
• TLS certificates
• High availability
• Experience with container networking and CNI plugins such as Calico or Cilium.
• Strong scripting skills using Bash and Python.
• Familiarity with Git and Infrastructure as Code principles.
• Ability to troubleshoot distributed systems and performance issues. Preferred Qualifications
• Experience with GPU clusters and AI infrastructure.
• Knowledge of NVIDIA drivers, CUDA, MIG, and DCGM.
• Experience with KubeVirt and GPU virtualization.
• Familiarity with Ceph, Longhorn, OpenEBS, NFS, or other persistent storage solutions.
• Experience with monitoring and observability tools including:
• Prometheus
• Grafana
• Loki
• Alertmanager
• Experience with ArgoCD, FluxCD, and GitOps workflows.
• Understanding of RDMA, RoCEv2, InfiniBand, and high-speed networking.
• Familiarity with Slurm and HPC environments.
• Experience with Rancher, OpenShift, or VMware Tanzu.
• Exposure to AI inference frameworks such as Triton, vLLM, SGLang, and TensorRT-LLM. Nice to Have
• CKA (Certified Kubernetes Administrator), CKS, or RHCE certifications.
• Experience supporting H100, H200, B200, A100, or HGX-based clusters.
• Familiarity with SONiC networking and NVIDIA Spectrum switches.
• Experience operating clusters with hundreds or thousands of GPUs.
• Infrastructure automation using Terraform or Ansible.
• Experience with hybrid cloud environments. What You'll Work On
• Large-scale GPU clusters for AI training and inference.
• Kubernetes-based AI platforms and multi-tenant environments.
• NVIDIA GPU Operator and containerized AI workloads.
• Distributed storage and high-performance networking.
• Infrastructure automation and GitOps.
• Hybrid cloud and on-prem AI deployments.
• Enterprise AI factories and next-generation GPU cloud infrastructure. Why Join Qubrid AI? At Qubrid AI, you'll help build the infrastructure behind the next generation of AI. You'll work with cutting-edge GPU clusters, Kubernetes, and NVIDIA technologies to power AI applications at scale for enterprises and developers worldwide.

If you enjoy Linux, Kubernetes, automation, and solving complex infrastructure challenges, we'd love to hear from you.