World Wide Technology Holding Co, LLC. (WWT) has a 12+ Months extendable opportunity available for a Domain Architect
- AI Storage @ Australia (Sydney / Melbourne ). Role: Domain Architect
- AI Storage Contract Duration: 12+ Months extendable contract chance ( With an option to get extension after 12 months)

Location:
Australia (Sydney / Melbourne)

About the Role
The Domain Architect
- AI Storage acts as the primary technical authority for the physical and logical lifecycle of high-performance data platforms across diverse client environments, who bridges the gap between architectural design and hands-on execution. You are a /"doer/" who is as comfortable configuring an NVMe-over-Fabrics connection in the CLI as you are explaining that configuration to a C-level client. As a System Integrator, we do not simply manage a static cloud; we design and deliver bespoke, high-scale AI factories for the world's leading enterprises. In this role, you will define the /"Gold Standard/" for storage infrastructure, moving beyond single-array management to architect repeatable, scalable, and automated data fabrics. You will serve as the technical lead for NVIDIA Cloud Provider (NCP) and private enterprise AI cloud deployments, owning the /"Storage/" in the critical /"Compute-Network-Storage/" triad. In this role, you will operate with a 60/40 split between delivering (60) complex AI infrastructure and providing Pre-Sales (40) Subject Matter Expertise (SME). You will lead the physical provisioning of high-throughput storage clusters for NVIDIA SuperPOD, NVIDIA BasePOD, and Cisco AI Factory environments, ensuring our clients receive /"Day 2/" ready AI factories, while assisting the sales team in defining the scope and cost of future deployments.

Key Responsibilities
1. Delivery & Implementation (60%)




- Parallel File System (PFS) Deployment:
- Lead the installation and configuration of high-performance storage clusters using technologies such as WEKA, DDN (Lustre), VAST Data, or Pure Storage.
- Optimise storage client configurations on compute nodes, managing kernel modules and mounting parameters to ensure stability at scale.
- Implement GPUDirect Storage (GDS) technologies to bypass the CPU and enable direct data paths between NVMe drives and GPU memory.
- Container Storage Integration:
- Deploy and configure Container Storage Interface (CSI) drivers for Kubernetes/Red Hat OpenShift, ensuring persistent storage is dynamically provisioned for AI workloads.
- Design storage classes that differentiate between /"Scratch/" (High Performance) and /"Home/Project/" (General Purpose) tiers.
- Performance Tuning & I/O Profiling:
- Execute synthetic benchmark suites (IOR, FIO, mdtest) to validate throughput (GB/s) and metadata performance (IOPS) against agreed SLAs.
- Troubleshoot /"straggler/" issues where slow I/O starves GPU utilisation, analysing client-side logs and fabric counters.
- Data Lifecycle Management:
- Implement automated data tiering strategies to move datasets between Hot (NVMe), Warm (QLC Flash), and Cold (Object/S3/Tape) tiers based on access frequency.
- Technical Scoping & Sizing:
- Lead the architectural sizing for storage opportunities.



Move the conversation beyond /"How many Petabytes?/" to /"How many Terabytes per Second per GPU?/".
- Calculate required performance for specific workloads (e.g., massive small-file ingestion for Computer Vision vs. large-file streaming for LLMs) to create accurate estimates.
- BoM Validation:
- Own the Storage Bill of Materials (BoM), ensuring the correct ratio of Storage Servers to Compute Nodes/GPUs.
- Validate interoperability between Storage Controllers, Host Channel Adapters (HCAs), and Transceivers against the NVIDIA Hardware Compatibility List (HCL) and vendor compatibility matrices.
- Advise clients on the transition from legacy Enterprise NAS (NFSv3) to modern AI-Native Storage protocols (NFS over RDMA, NVMe-oF).
- Design namespace architectures that support multi-tenancy, data sovereignty requirements, and potentially hybrid architectures for workload bursting capability.
- High-Performance Storage Ecosystem:
- Expert-level knowledge of Parallel File Systems (WEKA, Lustre, BeeGFS, GPFS).
- Deep understanding of Object Storage (S3 protocols) for model checkpointing and archiving.
- Mastery of storage protocols: NVMe-over-Fabrics (NVMe-oF), NFS over RDMA, and NVIDIA GPUDirect Storage (GDS).
- Deep understanding of the Linux I/O stack, including Block Device drivers, file system tuning (xfs, ext4), and client-side caching mechanisms.
- Infrastructure as Code (IaC):
- Proficiency in one of Python, Ansible, or Terraform for automating storage cluster deployment and client configuration management.
- Industry Background: Experience working within a System Integrator (SI), Storage Vendor (e.g. NetApp, Dell, Pure), or MSP environment.
- Platform Integration: Hands-on experience integrating storage with bare metal, and Kubernetes/Red

📌 AI Domain Architect (Melbourne)
🏢 World Wide Technology
📍 Melbourne