Technical Program Leader - AI Infrastructure
Designworks Talent · Full-time · Posted 5d ago
We’re partnering with an early-stage technology company building and scaling advanced AI infrastructure—and we’re looking for a Technical Program Leader who wants to be close to the action.
This is not a traditional PMO role. You’ll be operating at the intersection of AI infrastructure, engineering, data center deployment, and large-scale technical execution, helping turn ambitious infrastructure plans into production-ready environments.
You’ll work with engineering teams, infrastructure leaders, vendors, contractors, and operations to deliver complex GPU and data center programs from site readiness through commissioning and production handoff.
For the right person, this is an opportunity to join at an important stage of growth and help establish the execution discipline, processes, and infrastructure that will support significant expansion.
Lead complex AI infrastructure and data center deployment programs from planning through production handoff.
Coordinate work across site readiness, racks, cabling, power, cooling, networking, storage, and GPU infrastructure.
Translate technical requirements into clear deployment plans, milestones, readiness criteria, and deliverables.
Partner closely with engineering, infrastructure, facilities, operations, supply chain, OEMs, contractors, and vendors.
Own program schedules, dependencies, critical paths, risks, issues, actions, and key decisions and RAID
Drive commissioning, integrated systems testing, burn-in, validation, punch-list resolution, and operational readiness.
Identify blockers early and work across teams to keep complex deployments moving.
Provide concise updates to technical and executive leadership on progress, risks, trade-offs, and decisions.
Help establish repeatable processes for deploying AI infrastructure efficiently and reliably at scale.
You’re a technically credible program leader who understands infrastructure deployment firsthand. You don’t need to be the person configuring every server or switch, but you should be comfortable enough with the technology to challenge assumptions, understand dependencies, and earn the trust of engineering teams.
Required experience includes:
7+ years in data center infrastructure, technical deployment, mission-critical facilities, cloud, hyperscale, HPC, AI infrastructure, or a related environment.
Experience with several areas such as rack-and-stack, structured cabling, power, cooling, networking, storage, GPU infrastructure, commissioning, or operational readiness.
Experience leading technical workstreams and coordinating engineering teams, vendors, contractors, and other stakeholders.
Strong understanding of deployment planning, technical acceptance, defect management, change control, and production handoff.
Experience managing complex schedules, risks, dependencies, action items, and executive reporting.
Experience producing status reports, RAID logs, action trackers, readiness dashboards and leadership updates.
Experience in one or more of these areas is particularly valuable:
GPU infrastructure: NVIDIA HGX, DGX, or other accelerator and rack-scale platforms.
High-performance networking: InfiniBand, RoCE, high-speed Ethernet, cluster fabrics, or large-scale network deployments.
AI/HPC storage: High-throughput or parallel storage supporting training and other demanding workloads.
High-density infrastructure: Direct liquid cooling, rear-door cooling, CDUs, high-density racks, and complex power requirements.
Cluster bring-up: Firmware, BIOS, drivers, provisioning, node diagnostics, burn-in, thermal/power testing, and RMA management.
Performance acceptance: Cluster benchmarking, communications testing, ML/HPC workloads, node stability, and production acceptance.
Production operations: Slurm, Kubernetes, GPU fleet management, monitoring, telemetry, and operational runbooks.
You do not need experience across every technology listed. We’re looking for someone with meaningful depth in several areas who can lead effectively across the broader deployment lifecycle.
This is infrastructure built for modern AI workloads, not a conventional enterprise data center environment.
The programs may involve:
Multi-megawatt infrastructure and phased capacity deployments.
Hundreds or thousands of racks and significant GPU/accelerator capacity.
Multiple concurrent deployment workstreams or sites.
High-density rack designs, liquid cooling, high-speed networking, and complex power requirements.
Long-lead equipment, supply-chain constraints, and multiple technology vendors.
Cross-functional teams spanning engineering, facilities, supply chain, OEMs, contractors, and operations.
If you’ve delivered large-scale infrastructure and enjoy solving the problems that emerge when technology, facilities, vendors, and engineering all have to come together, this role will be highly relevant.
Technical credibility: You can engage directly with engineers and understand complex infrastructure decisions.
Execution mindset: You create structure without slowing teams down.
Hands-on leadership: You’re comfortable getting into the details when delivery is at risk and prioritize work onsite and keep delivering.
Strong communication: You can turn complex technical issues into clear actions and decisions.
Vendor accountability: You know how to manage scope, quality, milestones, and acceptance. You escalate issues or risks early and hold parties accountable.
End-to-end ownership: You stay engaged from initial planning through commissioning and production handoff.
Builder mentality: You enjoy working in an environment where processes are still evolving and you can help shape how things get done.
A degree in engineering, computer science, IT, data center operations, or a related technical field is preferred but not required.
Nice to have:
PMP, PRINCE2, ITIL, Uptime Institute, commissioning, data center, networking, cloud, GPU, or AI infrastructure certifications are a plus.
Related technology accreditations such as Cisco, Arista, NVIDIA, VMware, AWS, Azure or comparable infrastructure, network, cloud, GPU or AI platform certifications.
Bellevue, WA: Hybrid, with 3 days per week in the office for candidates within reasonable commuting distance.
Elsewhere in the U.S.: Fully remote for qualified candidates.
Travel: Up to 50% travel may be required
Work Eligibility: U.S. work authorization required; visa sponsorship is not currently available
France
Switzerland