AI Infrastructure and Kubernetes Platform

Job Description:

We are seeking a highly skilled AI Infrastructure and Kubernetes Platform Architect with deep expertise in managing GPU-accelerated workloads on NVIDIA DGX systems. The ideal candidate will have hands-on experience with Kubernetes at the administrator, application developer, and security levels (CKA, CKAD, CKS), and will be responsible for designing, deploying, securing, and maintaining large-scale AI infrastructure powered by DGX BasePODs and SuperPODs. This role involves optimizing AI workloads, managing high-performance networking (InfiniBand), and ensuring operational excellence across NVIDIA AI systems and BlueField DPU environments.

Key Responsibilities:

Kubernetes and 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.
• 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.
• Leverage DGX System Administration best practices for lifecycle management and updates.

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 and Compliance

• Apply best practices from the CKS certification to harden Kubernetes clusters and AI workloads.
• 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 metrics using GPU telemetry, Prometheus/Grafana, and NVIDIA DCGM.
• Troubleshoot and resolve complex system issues across hardware and software layers.

Required Skills and Qualifications:

• CKA, CKAD, CKS certifications demonstrating full-stack Kubernetes expertise.
• Proven 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, YAML.
• Familiarity with Base Command Manager, NVIDIA GPU Operator, and KubeFlow is a plus.
• Ability to work across teams to support ML researchers, DevOps engineers, and infrastructure teams.