NVIDIA AI Infrastructure & Kubernetes Platform Engineer (DGX Systems)

IT

New York, NY Contract Aug 12, 2026

NVIDIA AI Infrastructure & Kubernetes Platform Engineer (DGX Systems) Department: Infrastructure Engineering

Location / Remote Policy: Remote

Role Type: Contract — 6-month initial engagement

About Our Client Our client is a technology and professional services firm founded in 2015 on the strength of its founders’ 30 years of industry experience. They set out to bridge a gap in professional services — to be a true partner rather than just a vendor — delivering expert guidance, innovative solutions, and personalized service at a cost-effective rate. Their mission is to empower businesses to succeed in the digital era, harnessing technology to drive transformation, innovation, and growth. Guided by a “make a customer, not a sale” philosophy, they lead with a customer-first approach and a team of senior-level engineers sourced from the world’s leading OEMs, including AWS, Palo Alto Networks, Cisco, and Microsoft.

Job Description Our client is seeking a highly skilled AI Infrastructure & Kubernetes Platform Engineer with a proven track record deploying and managing NVIDIA DGX-based AI clusters, orchestrating containerized AI workloads on Kubernetes, and ensuring secure, high-throughput operations across InfiniBand-powered networks. You’ll bring a strong certification foundation across both Kubernetes (CKA, CKAD, CKS) and NVIDIA’s AI infrastructure stack, paired with hands-on experience across DGX, BlueField, and high-speed networking.

This role is central to supporting AI/ML infrastructure at scale — enabling efficient training and inference for complex models and integrating NVIDIA’s compute, storage, and fabric solutions with modern DevOps practices. Day to day, you’ll own DGX cluster operations, architect GPU-accelerated Kubernetes platforms, tune InfiniBand fabric for throughput, and harden the environment through DPU-enhanced security.

You’ll work at the intersection of infrastructure, DevOps, and AI/ML, keeping the platform reliable and cost-efficient for the teams that depend on it. The ideal candidate is deeply hands-on, obsessed with performance and security, and energized by operating some of the most advanced AI compute available.

Duties And 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 after deployments or hardware changes.

Kubernetes Platform Engineering

• Architect secure, scalable Kubernetes clusters optimized for GPU-accelerated workloads using the NVIDIA GPU Operator.
• Apply CKA/CKAD/CKS expertise 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 to offload storage, firewalling, and telemetry, strengthening AI workload security and performance.

Security & Compliance

• Apply CKS best practices to secure containerized AI environments.
• Configure runtime security, secrets management, network segmentation, and auditing across DPU-enhanced Kubernetes deployments.
• Support zero-trust 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 dashboards covering GPU utilization, thermal thresholds, and fabric health.

Required Experience/Skills Certifications

• Certified Kubernetes Administrator (CKA)
• Certified Kubernetes Application Developer (CKAD)
• Certified Kubernetes Security Specialist (CKS…