About the Job/Role

As an AI/HPC Platform Architect, you will be responsible for designing and building enterprise-grade platforms to support AI/ML and HPC workloads. This role focuses on Kubernetes, HPC cluster design, infrastructure hardware, and AI platform enablement, with delivery scope centered around HPE Private Cloud AI and HPE AI Factory solutions. Candidates with strong Kubernetes, HPC, or AI/ML expertise but limited hardware exposure may also be considered if they demonstrate trainability.

Roles & Responsibilities

Kubernetes Platforms:
- Manage and integrate container orchestration platforms (Red Hat OpenShift, SUSE Rancher, CNCF Kubernetes).
- Support GPU-accelerated workloads using NVIDIA GPU Operator and DCGM.
- Integrate Kubernetes with AI/ML workloads in hybrid or private cloud environments.
HPC Experience:
- Architect HPC clusters with GPU/compute nodes and HPC storage (Lustre, WEKA, Parallel Filesystems).
- Work with high-speed networking (InfiniBand, Mellanox, RoCE).
- Use HPC tools like HPE Cluster Management (HPCM) or NVIDIA Base Command Manager.
- Configure workload schedulers (Slurm, Altair PBS Pro).
Infrastructure & Hardware Stack:
- Administer Linux systems (RHEL, SLES, Ubuntu) and virtualization (KVM, OpenShift Virtualization).
- Work primarily with HPE servers and storage (Dell, Lenovo, Supermicro, NVIDIA DGX/HGX also acceptable).
- Apply compute, storage, and networking fundamentals in enterprise settings.
AI Platform Enablement:
- Deploy and support NVIDIA AI Enterprise and related AI/ML frameworks.
- Implement DevOps/MLOps practices (CI/CD, IaC, cloud-native security).
Project Delivery:
- Deliver infrastructure projects including platform build-outs for AI/ML and HPC.
- Collaborate with cross-functional teams to align technical solutions with business needs.

Required Qualifications

Proven expertise in Kubernetes, HPC, AI/ML workloads, and GPU integration.
Strong background in Linux administration, enterprise hardware, compute, storage, and networking.
Experience deploying and managing AI platforms (e.g., NVIDIA AI Enterprise).
Familiarity with DevOps/MLOps tools and practices.
Hands-on experience in infrastructure project delivery with a focus on AI/ML and HPC workloads.
Ability to work across teams, align solutions with business goals, and continuously learn new technologies.

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