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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Domain Architect - AI Compute (Nvidia Ecosystem) - **Company:** World Wide Technology - **Location:** Maryland Heights, MO, United States (Remote available) - **Experience:** Expert - **Salary:** $200,000.0 - $230,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Ubuntu (Operating System), Cloud Engineering, Nvidia CUDA, Data Centers, Linux, Network Interface Controllers, Firmware, Image Management, InfiniBand, Python (Programming Language), Machine Learning, OpenShift, PCI Express, Red Hat Enterprise Linux, Ansible, AI Infrastructure, Google Cloud, Large Language Models, cuDNN, Non-Uniform Memory Access, Containerization, Kubernetes, Information Technology, Bare Metal, Enterprise Integration, Slurm, NVIDIA Omniverse, Model Inference, Terraform, Oracle Cloud Infrastructure, Cisco, Nvme - **Published:** October 23, 2026 - **Apply:** https://www.techcareers.com/job.asp?id=3466384618&tx=VT6762TYD&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54 ## About the Role * 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 field, or equivalent practical experience. * Deep working knowledge of NVIDIA GPU platforms across the Hopper and Blackwell architectures, including Grace Hopper and Grace Blackwell superchip systems, and the associated software stack (CUDA, cuDNN, NCCL). * Strong Linux systems engineering skills, including distributions tuned for HPC and AI workloads (Ubuntu, RHEL), kernel tuning, driver management, and system hardening. * Automation and infrastructure-as-code experience, with proficiency in Ansible and Python, and familiarity with declarative tooling such as Terraform. * Demonstrated ability to translate technical architecture into commercial outcomes for senior stakeholders., * Industry background: experience within a systems integrator (SI) or managed service provider (MSP) environment. * Cluster management: hands-on experience with NVIDIA Base Command Managerand/or NVIDIA Mission Control. * Network integration: understanding of high-speed interconnects (InfiniBand NDR/HDR, RoCEv2) and how they interact with host PCIe and NVLink topologies. * Container platforms: Kubernetes and Red Hat OpenShift installation and administration. * Cloud architecture: AI-oriented compute design on AWS, Azure, Google Cloud, or OCI. * AI and orchestration tooling: exposure to Rafay, NVIDIA Run:ai, NVIDIA Omniverse, Red Hat OpenShift AI, and Kubeflow., WWT will consider for employment, without regard to disability, a disabled applicant who satisfies the requisite skill, experience, education, and other job-related requirements of the job and is capable of performing the essential requirements of the job with or without reasonable accommodation. World Wide Technology is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, sex (including pregnancy), sexual orientation, gender identity, national origin, age, disability, veteran status, genetic information, or other characteristics protected by law. We are committed to working with and providing reasonable accommodations to individuals with disabilities. If you have a disability and you believe you need a reasonable accommodation in order to search for a job opening or to submit an online application, please call 1-800-432-7008 and ask for Human Resources. ## Description 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.