AI Context Operations Lead

AI Context Operations Lead (Mercury)

Role overview

Own Mercury’s internal knowledge infrastructure—building a trusted context layer that captures what teams own, are building, and know. This context becomes the foundation for leadership reporting, operational reviews, planning, and the internal AI agents employees use every day.

Responsibilities

• Own the knowledge infrastructure: information architecture, taxonomy, governance, and a living, structured record of team knowledge.
• Build the knowledge layer on top of AI infrastructure by partnering with AI Engineering on:
  • schemas
  • automations
  • validation workflows so people and AI agents can retrieve and act on knowledge reliably.
• Own the reporting layer that turns shared context into operational insight, including:
  • leadership reporting
  • roadmap views and planning dashboards
  • reporting that powers company operating cadences.
• Drive company-wide adoption of standardized systems and practices by partnering across Engineering, Product, Design, Data, Compliance/Legal, Finance, Partnerships, Customer Support, and other teams.
• Continuously improve knowledge capture and usage by identifying operational friction, improving workflows, and ensuring employees have tooling and enablement to work effectively with AI.

Requirements

• 5–8 years experience in program/product operations, technical program management, product management, data, or similar roles, with a track record of driving company-wide systems or operational improvements.
• Strong ability as a systems designer / knowledge architect, turning messy, distributed information into simple, scalable structures people and AI systems can trust.
• Comfortable working with technical systems, including APIs, data models, analytics, and tools such as Linear and GitHub (also mentions Metabase).