Product Director - AI Infrastructure Platforms
On-site · Plano, Texas, United States
Job Summary
Product Director for AI Infrastructure Platforms responsible for setting the vision, strategy, and delivery of an enterprise-grade AI infrastructure platform spanning compute, storage, networking, and management services across on-prem, cloud, Neo Cloud, and edge. Drives go-to-market strategy, platform adoption, and capabilities toward AI Factory-scale operations. Manages relationships with senior stakeholders, cross-functional teams, and vendor ecosystems, translating rapid AI hardware changes into actionable roadmaps and investment cases (TCO, depreciation, ROI). Leads a team of Product Managers and Senior Product Associates to enable scalable, compliant, and cost-aware AI infrastructure across enterprise lines of business.
Required Qualifications
- 8+ years in technical product management or equivalent expertise delivering technology products at production scale
- Extensive experience in high-performance compute, GPU-accelerated, data center, or AI infrastructure
- Deep knowledge of the AI infrastructure stack across compute, accelerators, high-speed interconnects, networking, storage, and orchestration layers—with experience across technologies such as Nvidia GPUs (B200, H100, A100), InfiniBand, Spectrum-X, Arista, DataDirect Networks (DDN), VAST Data, and Kubernetes
- Understanding of how the AI infrastructure stack is integrated within AI Factory reference architectures and how architectural decisions impact performance, cost, and scalability at enterprise scale
- Drive alignment across engineering, security, compliance, and lines of business to deliver capabilities at business speed without compromising security or regulatory compliance
- Build and clearly communicate AI infrastructure investment cases, including TCO analysis, depreciation planning, and ROI modeling across on-prem, cloud, and hybrid deployments
- Build consensus and secure commitment to action across senior leadership on high-stakes technology and investment decisions
- Position AI infrastructure as a strategic enterprise asset, with a credible investment thesis and product roadmap progressing toward AI Factory–scale operations
- Qualify demand signals, prioritize investments across competing lines of business and multiple products, and balance GPU capacity, compliance requirements, and latency constraints
- Anticipate rapid shifts in AI hardware (accelerator evolution, emerging architectures, changing workload patterns) and translate them into actionable product strategy
- Own multi-year capital plans and vendor ecosystems; bring AI Factory stack depth (hardware selection, distributed training/inference, workload portability), and ensure regulatory constraints and security/compliance controls are designed across on-prem, cloud, and hybrid
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