Enterprise AI Platform - GPU & LLM Infrastructure Product Manager

About This Role

Define and lead the product strategy for Wells Fargo's enterprise-scale LLM/SLM inference GPU platform, partnering with engineering teams to develop a prioritized roadmap. Own capabilities across high-performance model inferencing, GPU orchestration, and platform services to enable secure, scalable, and enterprise-ready AI solutions.

Responsibilities

• Define and lead the product strategy for Wells Fargo's enterprise-scale LLM/SLM inference GPU platform.
• Partner closely with GPU hardware and platform engineering teams to translate customer needs and business objectives into a clear, prioritized roadmap with measurable outcomes.
• Own capabilities across high-performance model inferencing, GPU orchestration, and platform services, including vLLM, NVIDIA/Run:AI, and Red Hat OpenShift AI.
• Manage API productization, observability and evaluation, reliability and SLOs, and compliant end-to-end lifecycle management.
• Lead a team to identify, strategize and execute highly complex Artificial Intelligence initiatives that span a line of business.
• Recommend business strategy and deliver Artificial Intelligence enabling solutions to solve business challenges.
• Define and prioritize cases, obtain the required resources and ensure the solutions deliver the intended benefits.
• Leverage Artificial Intelligence expertise to evaluate technological readiness and resources required to execute the proposed solutions.
• Make decisions to drive the implementation of Artificial Intelligence initiatives and programs while serving multiple stakeholders.
• Resolve issues which may arise during development or implementation.

Requirements

• 5+ years of Artificial Intelligence Solutions experience
• 2+ years of hands-on experience with cloud platforms such as GCP or Azure
• 2+ years of hands-on experience with container orchestration technologies including Docker and Kubernetes/OpenShift

Qualifications

• 5+ years of Artificial Intelligence Solutions experience, or equivalent demonstrated through work experience, training, military experience, or education.

Nice to Have

• 2+ years of experience working on platform or ML/AI infrastructure products within regulated environments
• 2+ years of experience owning an API or platform with accountability for SLAs/SLOs, including versioning and deprecation strategies, change management, and reliability outcomes
• Strong communication skills, with the ability to influence senior stakeholders and clearly explain complex technical concepts to diverse audiences
• Working knowledge of LLM/SLM inference stacks, including vLLM, Triton, and TensorRT-LLM, as well as batching strategies, KV cache management, quantization techniques (e.g., FP8, INT4), and evaluation frameworks
• Familiarity with GPU and platform fundamentals, such as modern GPU architectures (e.g., H100/H200), MIG and NCCL, GPU orchestration tools (NVIDIA/Run:AI), and Kubernetes/OpenShift AI administration and admission control patterns
• Experience building developer-centric platforms, including APIs, SDKs, and structured release and governance processes
• Hands-on experience with observability and evaluation for GenAI systems, including dashboards, tracing, alerting, and safety and quality metrics
• Demonstrated strength in stakeholder management, partnering effectively across Risk, Security, Architecture, and line-of-business application teams

Skills

* Required skills

Benefits