Drive the product strategy and delivery of enterprise-grade AI infrastructure platforms that power model training, inference, and experimentation at JPMorganChase.
As Product Director for AI Infrastructure Platforms, you will set the vision, strategy, and direction for the platform's inferencing product area. The platform is an integrated suite of specialized AI infrastructure for model training, inference, and experimentation—consisting of compute, storage, network, and management services—with offerings that span public cloud, on-premises, Neo Cloud, and edge. You will define and deliver the go-to-market strategy, drive platform adoption across lines of business, and advance product capabilities toward an enterprise-scale AI Factory.
Success in this role requires navigating rapid AI hardware evolution, complex multi-vendor ecosystems, and the regulatory demands of a highly regulated global financial institution—delivering infrastructure for high-value AI use cases.
Job Responsibilities
- Own the product area vision, strategy, roadmap, execution, and business growth targets—with a strategic mandate to mature the platform toward an enterprise-scale AI Factory.
- Maximize returns on hardware capital investments spanning compute, accelerator, storage, networking, and orchestration layers—with direct accountability for driving platform adoption and utilization to realize the full value of the firm's AI infrastructure commitments.
- Serve as the key decision-maker for product prioritization and strategic direction across the AI Infrastructure product area—balancing competing demands, investment tradeoffs, and delivery sequencing across multiple products.
- Cultivate and manage senior stakeholder relationships to ensure alignment on vision, strategy, roadmap, and scope across the AI Infrastructure product area.
- Analyze market trends and interpret competitive signals to inform investment decisions, drive capital allocation, and direct vendor strategy across hyperscale providers (e.g. AWS, Azure, GCP), Neo Clouds (e.g. CoreWeave, Lambda Labs, Fluidstack, Runpod), edge platforms (e.g. Nvidia Jetson, HPE Edgeline, Dell Edge Gateway), and serverless GPU providers (e.g. Baseten, Together AI, Fireworks, Nebius).
- Identify and prioritize target market segments, qualify demand signals, and align product capabilities to address unmet customer needs across lines of business.
- Establish product metrics that quantify business value realization—spanning investment performance, cohort adoption, and platform utilization.
- Lead and develop a team of Product Managers and Senior Product Associates, providing strategic guidance and ensuring effective execution across the product area.
- Lead cross-functional teams and harness AI to drive efficiency and scalability in product management functions—enabling rapid prototyping, improving responsiveness to market demands, and amplifying productivity across a complex, matrixed environment.
Required Qualifications, Capabilities, and Skills
- 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.
Preferred Skills
- Advanced degree in Computer Science, Engineering, or related field; MBA or equivalent preferred.
- Background in technical product management within the AI infrastructure ecosystem—including infrastructure vendors, cloud providers, and system integrators—or within highly regulated industries such as financial services or healthcare.