Role overview
*NVIDIA AI Infrastructure & Kubernetes Platform Engineer (DGX Systems) Remote
Related Certifications required
6 months to 1+ yrs
$open
USC or GC req**
Alternate titles depending on context:
- AI Platform Architect – DGX & SuperPOD
- AI Infrastructure DevOps Engineer – NVIDIA DGX Stack
- *Senior AI Systems Engineer – DGX | Kubernetes | InfiniBand
Job Description:**
We are seeking a highly skilled AI Infrastructure & Kubernetes Platform Engineer with a proven track record in deploying and managing NVIDIA DGX-based AI clusters, orchestrating containerized AI workloads using Kubernetes, and ensuring secure, high-throughput operations across InfiniBand-powered networks. The ideal candidate will hold a combination of Kubernetes certifications (CKA, CKAD, CKS) and NVIDIA certifications (NCA-AIIO, NCP-AIO, NCP-AII, NCP-AIN), coupled with hands-on training in DGX, BlueField, and high-speed network operations.
This position plays a key role in supporting AI/ML infrastructure at scale, enabling efficient training and inference for complex models, and integrating NVIDIA's cutting-edge compute, storage, and fabric solutions with modern DevOps practices.
*Core Responsibilities:
AI Infrastructure Operations**
- Deploy and manage NVIDIA DGX BasePODs and SuperPODs for high-performance AI workloads.
- Oversee DGX system lifecycle operations including provisioning, monitoring, firmware upgrades, and capacity planning.
- Operate Base Command Manager to manage GPU clusters, schedule workloads, and integrate with MLOps tools.
- Perform DGX node health validation, NCCL interconnect testing, and NVLink topology verification following new deployments or hardware changes.
What we're looking for
- Kubernetes, Helm, GPU Operator, Kubeflow
- DevOps tools: Ansible, Terraform, GitOps, CI/CD pipelines
- Storage: NFS, BeeGFS, Lustre
- Networking: RoCE, InfiniBand, DPU offload, gRPC, RDMA
- Programming/scripting: Python, YAML, Bash