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World Wide Technology (WWT) strives to make a new world happen. WWT's work benefits clients and partners as much as it does its people and community across the globe.

Founded in 1990, WWT brings together strategy, deep technical expertise and world-class partnerships to help public and private sector organizations design, build and scale intelligent AI, digital, cybersecurity, cloud and infrastructure solutions. Through its Advanced Technology Center (ATC)-a collaborative ecosystem featuring state-of-the-art hardware and software-WWT enables clients and partners to conceptualize, test and validate innovative technology and then deploy solutions at scale using its global integration and distributions capabilities.

With more than 14,000 team members and over 60 locations globally, WWT's culture-grounded in core values and leadership philosophies-has been recognized by Fortune® and Great Place to Work for its commitment to innovation, trust and creating a great place to work for all. WWT provides products and services to large enterprise, global service provider and public sector clients in up to 130 countries across six continents. Softchoice, a World Wide Technology company, supports commercial and SMB markets in the U.S. and Canada.

Want to work with highly motivated individuals on high-performance teams? Join WWT today!

What is the Solutions Consulting & Engineering Team and why join?

Solutions Consulting & Engineering is an organization that is customer-focused and solutions-led. We deliver end-to-end and emerging solutions to drive customer satisfaction and increase profitability and growth. Our world-class management consulting, delivery excellence, and engineering brilliance enable our success. We embody the OneWWT mindset by bringing the right talent at the right time from anywhere within WWT to solve our customer's problems. Our goal is to bring together business acumen with full-stack technical know-how to develop innovative solutions for our clients' most complex challenges.

About the Role

AI Factory Overview

WWT's AI Factory practice designs, builds, and delivers purpose-built AI infrastructure environments that enable organizations to deploy, scale, and operationalize advanced AI workloads. By integrating compute, networking, storage, software, and operational expertise into a validated reference architecture, the AI Factory team helps customers accelerate AI adoption while reducing deployment risk and complexity.

As Domain Architect - AI Compute , you will serve as the technical authority for the design, deployment, and lifecycle management of high-performance AI compute infrastructure within WWT's AI Factory practice. Working closely with networking, storage, and software specialists, you will define and deliver scalable GPU-based platforms that form the foundation of modern AI factories, ensuring customers receive reliable, automated, and production-ready environments.

You will bridge architecture and execution, providing deep expertise across NVIDIA and partner compute platforms while guiding customers through both implementation and operational readiness. As a trusted advisor and hands-on technical leader, you will help shape AI Factory solutions, support strategic client engagements, and establish best practices that drive successful AI infrastructure outcomes.

Your time will be split roughly 60/40 between delivering complex AI infrastructure (60%) and providing pre-sales subject matter expertise (40%). You will lead the physical provisioning of NVIDIA DGX SuperPOD, NVIDIA DGX BasePOD, and Cisco AI POD environments, ensuring clients inherit platforms that are genuinely ready for day-2 operations, while helping the sales team scope and cost future deployments.

Key Responsibilities

Delivery and implementation

Bare-metal build and provisioning

+ Lead the physical provisioning of GPU platforms and clusters, including NVIDIA GB200/GB300 NVL72 rack-scale systems, NVIDIA DGX SuperPOD and DGX BasePOD reference architectures, HGX- and MGX-based systems, and Cisco AI PODs.

+ Use NVIDIA Base Command Manager (BCM) for cluster provisioning, diskless boot, image management, and firmware lifecycle management.

+ Harden and baseline the host operating system across the fleet.

+ Establish cluster monitoring and lifecycle management with NVIDIA Mission Control.

+ Build and execute zero-touch provisioning (ZTP) workflows that turn bare-metal hardware into production-ready nodes.

Scheduler and workload configuration

+ Define and enforce fair-share policies, fractional GPU allocation using Multi-Instance GPU (MIG), and preemption logic for multi-tenant environments.

+ Ensure orchestration layers respect hardware topology, including NUMA affinity and PCIe topology, to protect performance.

+ Implement and tune advanced schedulers: Slurm on bare metal, and NVIDIA Run:ai, Kueue, or Volcano on Kubernetes.

Orchestration and day-2 operations

+ Deploy and configure multi-cluster management and observability platforms such as Rafay.

+ Implement high-fidelity telemetry using NVIDIA Data Center GPU Manager (DCGM) to monitor GPU health, thermal throttling, and Xid error rates.

+ Lead the transition to day-2 operations, ensuring the environment is fully integrated with the client's identity providers and storage backends before handoff.

Performance engineering

+ Conduct acceptance and validation testing using NCCL tests and HPL to verify cluster performance against expected baselines.

+ Perform kernel and OS-level tuning (hugepages, sysctl, and driver parameters) to optimize for high-bandwidth InfiniBand and RoCEv2 fabrics.

Pre-sales SME and consulting

Technical scoping and estimation

+ Support the sales team by validating customer technical requirements and producing accurate level-of-effort (LOE) estimates for statements of work.

+ Define the standard operating environment (SOE) used in proposals to ensure repeatability across engagements.

Bill of materials validation and architecture

+ Own the technical accuracy of the compute bill of materials.

+ Verify that all components, including memory, NVMe storage, NICs, and optical transceivers, are aligned with NVIDIA-Certified Systems requirements and the qualified component lists in the relevant DGX, HGX, MGX, and NVL72 reference architectures.

+ Match GPU platform selection to the client's actual workload profile and growth expectations.

Client workshops

+ Lead technical discovery workshops to determine workload requirements, distinguishing between the very different infrastructure profiles of traditional machine learning, LLM training, LLM inference, and Omniverse digital twin and visualization workloads.

Minimum Qualifications

+ 10+ years in HPC, AI, or data center infrastructure engineering, including 7+ years in a customer-facing architecture role.

+ Bachelor's degree in Computer Science, Engineering, or a related...

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Domain Architect - AI Compute (Nvidia Ecosystem)

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