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.

2. Pre-Sales SME & Consulting (40%)

- 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.
- Client Strategy Workshops:
- Advise clients on the transition from legacy Enterprise NAS (NFSv3) to up-to-date 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.

Technical Competencies

Essential Skills

- 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) .
- Linux I/O Engineering:
- 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.

Desirable Experience

- 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 Hat OpenShift .
- Network Affinity: Understanding of InfiniBand and RoCEv2 fabrics from a storage perspective (Congestion Control, Quality of Service).
- LLM Inference & Caching Stack:
- Understanding of technologies such as NVIDIA Dynamo , vLLM , SGLang , for distributed inference serving ad KV cache management.
- Understanding of Distributed Pre-fill concepts and their storage I/O requirements.
- Understanding LMCache for KV cache offloading and sharing.

Success Metrics (KPIs)

1. I/O Saturation: Achieving >90% of the theoretical wire-speed throughput on compute clients during validation testing (IOR/FIO).
2. Deployment Velocity: Successful "Day 1" mount availability across all compute nodes using automated playbooks.
3. Storage BoM Accuracy : Usable capacity vs. Raw capacity calculations are accurate to within 5% of client requirements (i.e. accounting for product functional overhead).

📌 Domain Architect AI Storage (Sydney)
🏢 World Wide Technology
📍 Sydney