AI Product Operations Lead

Role overview

The AI Product Operations Lead establishes and runs an internal AI operations capability for a research and development organization, converting ad hoc experimentation into reliable, adopted systems. Sitting between AI strategy and day-to-day execution, the role blends systems thinking, user discovery, and hands-on technical work to deliver practical solutions across product development. It suits someone who enjoys taking ambiguous problems from idea through sustained operation, not just prototyping or writing requirements.

Responsibilities

- Build and continuously maintain a backlog of AI opportunities, helping teams identify, prioritize, and sequence the workflows where AI can deliver the greatest impact.

- Take ambiguous problems from discovery through delivery: understand users and workflows, explore options and trade-offs, design the right approach, coordinate or build implementation, test, document, launch, measure, and improve over time.

- Design simple, AI-native solutions that fit naturally into how people work, choosing between prompts, automations, integrations, agents, databases, or custom applications based on the problem at hand.

- Maximize value from enterprise AI tools in partnership with IT and security teams, covering access, configuration, connectors, enablement, and ongoing support.

- Own the full lifecycle of AI systems within the portfolio, including automations, agents, internal web applications, and the evaluation, testing, reliability, observability, security, and maintenance practices that keep them healthy.

- Contribute to broader AI governance and enablement efforts, improve visibility into active work, risks, adoption, and outcomes, and explore autonomous agents that can improve how teams plan, discover, design, build, evaluate, and manage work.

Requirements

- Practical experience applying AI to real problems by building solutions that people actually use, not only experimenting with models.

- Track record of taking ambiguous problems from discovery through implementation, testing, adoption, and ongoing improvement.

- Strong systems thinking: ability to reason about people, processes, tools, data flows, integrations, permissions, and technical constraints, and to make thoughtful trade-offs.

- Solid user and workflow discovery skills, with the judgment to uncover the real job to be done and simplify solutions when they have grown too complex.

- Hands-on technical fluency building, modifying, and troubleshooting internal workflows, automations, agents, or applications, along with comfort using repositories, pull requests, application hosting, deployment, logging, and operational troubleshooting.

- Experience managing work independently, including prioritizing a backlog, handling dependencies and risks, documenting decisions, and communicating clearly with stakeholders.

- Sound judgment around responsible AI, security, access, and governance, knowing when to bring in specialist partners.

Nice to have

- Experience running or contributing to AI governance or enablement programs that give leaders visibility into active work, risks, adoption, and outcomes.

- Familiarity partnering with finance and operations to improve visibility into AI tool usage, spend, and value, informing future investment decisions.