Senior AI Product Manager - Data Center Engineering and Operations

NVIDIA

1d•$208,000 - $327,750

About The Position

Join NVIDIA as a Senior AI Product Manager - Data Center Engineering and Operations, and contribute to pushing the boundaries of AI technology. This is your chance to create the next generation in managing data center environments with innovative and high-quality AI solutions! The Senior AI Product Manager will guide the entire lifecycle of AI-powered features and workflows within our proprietary software and automation systems. This role emphasizes AI solutions throughout the full data center lifecycle: from capacity and infrastructure planning to equipment procurement, asset intake and tracking, installation, maintenance, and decommissioning. You will join forces with software and data engineers, UI/UX designers, and data center associates. Together, this effort will make AI capabilities visible, trustworthy, and embedded in routine tasks.

Requirements

BS or MS degree in Computer Science, Computer Engineering, or equivalent experience.
12+ years in product management, software engineering or similar roles at a technology company serving enterprises
Practical experience with AI/ML application techniques.
Experience working with Data Center Engineering and Operations infrastructure, data, and automated workflows
Background with managing vendor data pipelines such as ASN solutions and shop floor configuration details
Experience using AI coding tools like Cursor for prototyping
Proven success in leading engineering efforts using Agile sprints

Nice To Haves

Experience using Agile methodology and ceremonies
Background using data from ERP systems like SAP for procurement and asset management.
Experience performing or supporting data center operations roles such as procurement, site logistics, asset management or DC Ops technician activities.

Responsibilities

Direct the product roadmap for AI features integrated within internal tools across the data center lifecycle (planning, procurement, deployment, operations, and decommissioning).
Identify new opportunities to deploy AI solutions using existing data.
Prioritize opportunities based on feasibility and impact.
Assemble user requirements for features and improvements driven by artificial intelligence.
Conduct discovery sessions with internal collaborators (data center engineers, operations leads, planning/procurement teams) to understand workflows, difficulties, and where AI can help or automate processes.
Transform these insights into detailed product requirements and user stories, for AI features.
Work closely with development and UX teams to guarantee AI functions are easily comprehended within existing internal tools and dashboards.
Incorporate clear confidence indicators, explanations, and controls.
Establish and sustain effective feedback channels with internal users.
Identify user-facing controls and review procedures suitable for operational settings (e.g., suggestions needing human approval, override/rollback options, feedback on inaccurate recommendations).