AI Infrastructure and Kubernetes Platform Architect Remote
About The Position
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.
Requirements
- 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.
- Ability to work across teams to support ML researchers, DevOps engineers, and infrastructure teams.
Nice To Haves
- Familiarity with Base Command Manager, NVIDIA GPU Operator, and KubeFlow is a plus.
Responsibilities
- 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 resource partitioning strategies using MIG (Multi-Instance GPU) to maximize hardware utilization across concurrent AI workloads.
- Implement and manage Helm charts, custom controllers, and GPU operators for scalable ML infrastructure.
- 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.
- Coordinate capacity planning for DGX cluster expansion including rack power, cooling, and storage integration with NVIDIA AI Enterprise software stack.
- 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.
- 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.
- 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.
- Implement MLOps workflows integrating KubeFlow Pipelines, NVIDIA Triton Inference Server, and model registry tooling to support end-to-end model training and production deployment.
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Career Level
Mid Level
Education Level
No Education Listed
