Important Notes Before Applying

- Read everything carefully. The requirements and screening questions are critical. Applications that do not answer them correctly and satisfactorily will be automatically rejected.
- Work from home.
- Full-time role: this position requires complete commitment. Candidates planning to work two or more jobs simultaneously, or seeking part-time work, should not apply - such applications will be automatically rejected.
- Working hours: late nights in India, until approximately 4 AM IST, to overlap with US working hours. Do not apply if this schedule does not work for you.
- Compensation: depends on experience and current verifiable compensation (paychecks).
- Experience: junior candidates with 2+ years of strong hands-on experience are suitable.

About Qubrid AIQubrid 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. About the RoleWe are looking for a hands-on GPU Kubernetes Cluster Engineer with deep Linux, Kubernetes, cluster automation, and Helm package development expertise to deploy, manage, automate, and optimize GPU clusters used for AI training and large-scale inference workloads.

This role is for someone who has operated real production Kubernetes clusters - not just run local demos or followed tutorials. You will build and run highly available Linux-based GPU clusters, write the Helm charts and automation that deploy them, and own reliability across the stack.

The ideal candidate is comfortable working across:

- Production Kubernetes cluster deployment and administration
- Linux-based GPU clusters for training and inference
- Helm chart and package development for platform and AI workloads
- Cluster automation for provisioning, upgrades, and day-2 operations
- NVIDIA GPU Operator, drivers, CUDA, and container runtimes
- Container networking, ingress, and high-performance networking
- Persistent and distributed storage
- Monitoring, logging, and alerting
- GitOps and Infrastructure as Code

You will work closely with GPU engineers, AI platform teams, and customers to deliver reliable, scalable infrastructure for AI workloads.

Responsibilities

- Kubernetes Cluster Engineering
- Deploy, configure, and maintain production Kubernetes clusters for AI and GPU workloads
- Bootstrap and manage multi-node clusters using kubeadm
- Configure Kubernetes scheduling, namespaces, resource quotas, node pools, taints, tolerations, and affinity rules
- Design and operate multi-tenant Kubernetes environments
- Configure high availability and perform cluster upgrades with minimal downtime
- Manage container runtimes including containerd and Docker
- Configure container networking and CNI plugins such as Calico or Cilium
- Manage ingress controllers, load balancing, DNS, and TLS certificates

- Helm Package Development

- Design, write,



and maintain production-grade Helm charts for platform services and AI workloads
- Build reusable, parameterized charts with clean values structures, templates, helpers, and dependencies
- Package GPU-enabled workloads (inference servers, model services, operators) as versioned Helm releases
- Manage chart versioning, chart repositories, and release lifecycles (install, upgrade, rollback)
- Implement chart testing, linting, and validation before release
- Deploy and manage workloads using Helm, Operators, and GitOps methodologies

- Cluster Automation

- Automate cluster provisioning, configuration, and scaling end to end
- Automate Kubernetes upgrades, node onboarding, and day-2 operations
- Build automation using Ansible, Bash, and Python
- Implement GitOps workflows for declarative cluster and application management
- Apply Infrastructure as Code principles to make cluster builds repeatable and auditable
- Reduce manual operational work through scripts, pipelines, and self-healing patterns

- GPU Infrastructure

- Build and operate Linux-based GPU clusters supporting training and inference environments
- Install and manage NVIDIA GPU Operator, drivers, CUDA, and GPU-enabled container runtimes
- Configure GPU scheduling, sharing, and partitioning for AI workloads
- Collaborate with GPU engineers and AI platform teams to optimize infrastructure for inference and training

- Storage, Networking & Observability

- Manage persistent storage solutions and distributed file systems
- Troubleshoot Kubernetes, Linux, networking, and storage issues across distributed systems
- Implement cluster monitoring, logging, and alerting
- Diagnose and resolve performance bottlenecks in GPU and cluster environments

- Operations & Customer Deployments

- Participate in customer deployments across cloud, hybrid, and on-prem environments
- Provide operational support for production clusters
- Write technical documentation, runbooks, and deployment guides
- Take ownership of infrastructure from initial build through production operation and maintenance

Requirements

Must-Have
- 4+ years of Linux systems administration experience
- 3+ years of hands-on Kubernetes administration experience
- Strong expertise with:
- Kubernetes
- kubeadm
- containerd
- Helm (including writing and maintaining your own charts)
- Docker
- Linux (Ubuntu, Rocky Linux, RHEL)
- Proven experience developing Helm charts and packaging applications for Kubernetes
- Experience automating cluster provisioning and operations
- 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 in Bash and Python
- Familiarity with Git and Infrastructure as Code principles
- Ability to troubleshoot distributed systems and performance issues

Preferred
- 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
- Experience with Kubernetes Operators or custom controllers
- 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
- Helm charts and packaging for AI platform services and GPU workloads
- Cluster automation and GitOps-driven operations
- NVIDIA GPU Operator and containerized AI workloads
- Distributed storage and high-performance networking
- Hybrid cloud and on-prem AI deployments
- Enterprise AI factories and next-generation GPU cloud infrastructure

What We're Looking For
- Someone who has run production Kubernetes clusters, not just deployed to them
- Someone who writes Helm charts and automation, not just installs existing ones
- A solid troubleshooter who enjoys debugging complex distributed systems
- An automation-first mindset - if it's done twice, it gets scripted
- An ownership mentality with strong execution skills
- Someone comfortable working across Linux, Kubernetes, networking, storage, and GPUs without heavy supervision
- Someone who understands that reliable AI infrastructure requires more than spinning up a cluster - it requires thoughtful automation, packaging, observability, and operational discipline

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.

📌 GPU Kubernetes Cluster Engineer WFH (India)
🏢 Qubrid AI
📍 India