AI Infrastructure and Kubernetes Platform Architect – DGX Systems

IT

New York, NY Contract Aug 12, 2026

AI Infrastructure and Kubernetes Platform Architect – 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 and Kubernetes Platform Architect with deep expertise in managing GPU-accelerated workloads on NVIDIA DGX systems. In this role, you’ll design, deploy, secure, and maintain large-scale AI infrastructure powered by DGX BasePODs and SuperPODs — owning the platform that ML researchers and engineers rely on to train and ship models.

Day to day, you’ll architect and run containerized AI/ML platforms on Kubernetes, optimize GPU utilization across concurrent workloads, manage high-performance InfiniBand networking, and harden the environment against security threats. Your work directly impacts how efficiently the organization’s most expensive compute assets are used and how reliably its AI teams can operate.

You’ll collaborate across teams — supporting ML researchers, DevOps engineers, and infrastructure groups — and serve as the technical authority on NVIDIA AI systems, from Base Command Manager scheduling to BlueField DPU integration. This role exists to bring operational rigor, automation, and security to a rapidly scaling AI compute environment.

Duties And Responsibilities Kubernetes & AI Platform Orchestration

• Architect and maintain containerized AI/ML platforms using Kubernetes on DGX systems.
• Integrate NVIDIA Base Command Manager with Kubernetes for workload scheduling and GPU resource optimization.
• Design multi-tenant GPU partitioning strategies using MIG (Multi-Instance GPU) to maximize utilization across concurrent workloads.
• Implement and manage Helm charts, custom controllers, and GPU operators for scalable ML infrastructure.

DGX Infrastructure Administration

• Administer and optimize NVIDIA DGX BasePODs and SuperPODs.
• Ensure optimal GPU, CPU, and storage performance across AI clusters.
• Apply DGX System Administration best practices for lifecycle management and updates.
• Coordinate capacity planning for cluster expansion, including rack power, cooling, and storage integration with the NVIDIA AI Enterprise stack.

High-Performance Networking & DPU

• Deploy, monitor, and manage InfiniBand networks using Unified Fabric Manager (UFM).
• Integrate BlueField DPUs for offloaded security, networking, and storage tasks.
• Optimize end-to-end data pipelines from storage to GPUs.

Security & Compliance

• Harden Kubernetes clusters and AI workloads using CKS best practices.
• Implement secure service mesh and microsegmentation with BlueField DPU integration.
• Conduct regular audits, vulnerability scanning, and security policy enforcement.

Automation & Monitoring

• Automate deployment pipelines and infrastructure provisioning with IaC tools (Terraform, Ansible).
• Monitor performance using GPU telemetry, Prometheus/Grafana, and NVIDIA DCGM.
• Troubleshoot and resolve complex issues across hardware and software layers.
• Build MLOps workflows integrating KubeFlow Pipelines, NVIDIA Triton Inference Server, and model registry tooling to support end-to-end training and production deployment.

Required Experience/Skills

• CKA, CKAD, and CKS certifications — demonstrating full-stack Kubernetes expertise.
• Proven hands-on experience with NVIDIA DGX systems and AI workload orchestration.
• Hands-on expertise in InfiniBand networking, UFM, and BlueField DPU administration.
• Strong scripting and automation skills in Python, Bash, and YAML.
• Ability to work across teams to support ML researcher…