Domain Architect - Ai Storage- 12M Contract - Perm

Northbridge It Recruitment; Melbourne; Hybrid; AUD 200,000 - 260,000; Full time

Job summary

Northbridge It Recruitment in Melbourne invites a Domain Architect - AI Storage to design, deploy and optimise storage for large-scale AI factories and GPU clusters.

You will lead HPC storage, NVMe-oF, GDS, and parallel file systems, collaborating with compute and network teams to deliver scalable AI infrastructures.

This hands-on role requires strong Linux, automation (Python/Ansible/Terraform) and experience with NVIDIA SuperPOD, DGX and Cisco AI Factory deployments.

Qualifications

• Extensive experience delivering high-performance storage solutions in HPC, AI, or large-scale enterprise environments.
• Hands-on knowledge of parallel file systems such as WEKA, Lustre, BeeGFS or GPFS.
• Experience architecting storage for GPU clusters, AI training and/or inference workloads.
• Strong understanding of storage performance, throughput, IOPS, metadata performance and Linux I/O optimisation.
• Knowledge of NVMe-oF, RDMA-based storage and high-speed data access architectures.
• Experience with object storage and S3-compatible protocols for datasets, model checkpoints and archival workloads.
• Strong Linux systems knowledge, including file systems, block devices, client configuration and performance troubleshooting.
• Automation experience using Python, Ansible or Terraform.
• Ability to design scalable storage architectures and translate technical requirements into reliable, production-ready deployments.
• Experience deploying storage for NVIDIA SuperPOD, BasePOD, DGX/HGX GPU clusters or Cisco AI Factory environments.
• Hands-on experience with NVIDIA GPUDirect Storage (GDS).
• Integration with Kubernetes, Red Hat OpenShift and CSI storage drivers.
• Understanding of InfiniBand and RoCEv2 networking from a storage performance perspective.

Responsibilities

• Design, deploy and optimise HPC and AI storage infrastructure for GPU clusters.
• Lead high-performance parallel file systems and storage platforms (WEKA, Lustre, BeeGFS, GPFS, Pure).
• Architect high-throughput storage fabrics to support AI training and inference at scale.
• Implement NVMe-over-Fabrics, GDS, NFS over RDMA, and S3-compatible storage.
• Integrate storage with Kubernetes, OpenShift, and CSI drivers.
• Develop automation for deployment using Python, Ansible or Terraform.
• Benchmark and troubleshoot storage performance using IOR, FIO, mdtest.
• Design data lifecycle and tiering strategies across NVMe, flash and object storage.
• Collaborate with compute and network architects for end-to-end performance.
• Lead technical implementation, testing and validation of storage solutions.

Skills

High-performance storage

WEKA Lustre BeeGFS GPFS

NVMe-oF

GDS

NFS over RDMA

S3-compatible storage

Kubernetes

OpenShift

CSI drivers

Python

Ansible

Terraform

GPU clusters

AI storage

Tools

WEKA

Lustre

BeeGFS

GPFS

NVMe-oF tooling

Job description

ACT NOW: Opportunity to join a market leading System Integrator delivering Multi-Billion-Dollar AI Data Centre Upgrade Programs. Work with the New and Emerging technology such as NVIDIA in an Org organisation at the forefront of delivering AI Data Centre Upgrade Programs, Delivering GPUs at scale.

We have a (12-Month Contract - Permanent) Domain Architect - AI Storage role available.

You will design, deploy and optimise high-performance storage infrastructure for large-scale AI factories, GPU clusters and High-Performance Computing (HPC) environments.

This is a hands-on architecture and delivery role focused on HPC storage, GPU data platforms, parallel file systems and high-throughput storage fabrics.

You will lead the storage component of NVIDIA SuperPOD, BasePOD and Cisco AI Factory deployments, ensuring storage infrastructure can feed large GPU clusters at scale and support demanding AI training, inference and enterprise workloads.

You will define repeatable storage architectures and technical standards for NVIDIA Cloud Provider (NCP) and private enterprise AI cloud environments, working closely with compute and network architects to deliver fully integrated AI infrastructure.

Key responsibilities

• Design, deploy and optimise HPC and AI storage infrastructure for GPU clusters and large-scale AI factories.
• Lead implementation of high-performance parallel file systems and storage platforms, including WEKA, DDN/Lustre, VAST Data, BeeGFS, GPFS and Pure Storage.
• Architect high-throughput storage fabrics capable of supporting AI training, inference, checkpointing and large-scale dataset processing.
• Implement and optimise NVMe-over-Fabrics (NVMe-oF), NVIDIA GPUDirect Storage (GDS), NFS over RDMA and S3-compatible object storage.
• Integrate storage with bare-metal GPU servers, Linux compute nodes, Kubernetes and Red Hat OpenShift.
• Configure Container Storage Interface (CSI) drivers, storage classes and persistent storage for AI workloads.
• Benchmark and troubleshoot storage performance using tools such as IOR, FIO and mdtest, identifying I/O bottlenecks that impact GPU utilisation.
• Develop automated deployment and configuration processes using Python, Ansible or Terraform.
• Design data lifecycle and tiering strategies across high-performance NVMe, flash and object storage.
• Collaborate with compute and network architects to ensure end-to-end performance across the Compute-Network-Storage architecture.
• Lead technical implementation, performance tuning, testing and validation of storage solutions.

Essential skills and experience

• Extensive experience designing and delivering high-performance storage solutions across HPC, AI, GPU or large-scale enterprise environments.
• Strong hands-on knowledge of parallel file systems such as WEKA, Lustre, BeeGFS or GPFS.
• Experience architecting storage for GPU clusters, AI training and/or inference workloads.
• Deep understanding of storage performance, throughput, IOPS, metadata performance and Linux I/O optimisation.
• Knowledge of NVMe-oF, RDMA-based storage and high-speed data access architectures.
• Experience with object storage and S3-compatible protocols for datasets, model checkpoints and archival workloads.
• Strong Linux systems knowledge, including file systems, block devices, client configuration and performance troubleshooting.
• Automation experience using Python, Ansible or Terraform.
• Ability to design scalable storage architectures and translate technical requirements into reliable, production-ready deployments.
• Experience deploying storage for NVIDIA SuperPOD, BasePOD, DGX/HGX GPU clusters or Cisco AI Factory environments.
• Hands-on experience with NVIDIA GPUDirect Storage (GDS).
• Integration with Kubernetes, Red Hat OpenShift and CSI storage drivers.
• Understanding of InfiniBand and RoCEv2 networking from a storage performance perspective.