Job Description

NVIDIA AI Infrastructure & Kubernetes Platform Engineer (DGX Systems)

Related Certifications required

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.

Kubernetes Platform Engineering

• Architect secure and scalable Kubernetes clusters optimized for GPU-accelerated workloads using NVIDIA GPU Operator.
• Leverage expertise from CKA/CKAD/CKS to develop, deploy, and secure AI applications on Kubernetes.
• Implement CI/CD pipelines and GitOps methodologies for deploying and managing ML workflows.

High-Performance Networking & DPUs

• Administer InfiniBand networks and BlueField DPUs using Unified Fabric Manager (UFM) .
• Enable NVLink/NVSwitch performance across GPU nodes and tune fabric configurations for minimal latency and maximum throughput.
• Use BlueField for offloading storage, firewalling, and telemetry, enhancing AI workload security and performance.

Security & Compliance

• Apply best practices from the CKS certification to secure containerized AI environments.
• Configure runtime security, secrets management, network segmentation, and auditing using DPU-enhanced Kubernetes deployments.
• Support zero-trust architecture initiatives by enforcing workload identity, RBAC policies, and supply chain integrity across AI container images and model artifacts.

Monitoring, Telemetry & Optimization

Monitor GPU, CPU, and I/O performance using NVIDIA DCGM , Prometheus, Grafana, and Base Command APIs.

• Tune system performance and model training pipelines for cost-efficiency and throughput.
• Build and maintain operational runbooks, incident response playbooks, and SLA reporting dashboards covering GPU utilization, thermal thresholds, and fabric health.

Qualifications:

Certifications a plus:

• Certified Kubernetes Administrator (CKA)
• Certified Kubernetes Application Developer (CKAD)
• Certified Kubernetes Security Specialist (CKS)
• NVIDIA Certified Associate: AI Infrastructure & Operations (NCA-AIIO)
• NVIDIA Certified Professional: AI Infrastructure (NCP-AII)
• NVIDIA Certified Professional: AI Operations (NCP-AIO)
• NVIDIA Certified Professional: AI Networking (NCP-AIN)

Expertise With:

• DGX System, BasePOD, and SuperPOD Administration
• BlueField DPU Configuration & Operations
• InfiniBand Fabric and UFM Management
• Base Command Manager for workload orchestration

Technical Skills:

• 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

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