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AI Product Lead, Internal Agents

vermillion analytics • United State
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Lead the development and deployment of internal AI agents that automate end-to-end business processes and push code to production. Own context architecture, design integrations, and create evaluation suites to monitor and improve agent performance. Requires 5-15 years of experience as a Technical Product Manager or founder in B2B SaaS or AI startups with strong coding or AI tooling proficiency.

Key Highlights
Build and ship internal AI agents that handle entire jobs end-to-end, including code production and knowledge management.
Own context architecture and design integrations/MCPs to make internal tooling accessible to agents.
Create evaluation suites and monitor agent trajectories to iterate on performance and reliability.
Key Responsibilities
Building and shipping internal AI agents that push code to production, manage company knowledge, and handle whole jobs end-to-end.
Owning context architecture: defining sources of truth and keeping documentation in sync with reality.
Designing integrations and MCPs that make internal tooling accessible to agents.
Creating evaluation suites, monitoring agent trajectories, and iterating to improve performance.
Inventing new infrastructure, interfaces, and operating models for human-agent collaboration.
Technical Skills Required
AI Agents Product Management Coding
Benefits & Perks
$175K–$275K base salary
Highly competitive equity
Unlimited token budget for coding agents
Nice to Have
Former founder or very early PM hire
Experience working with engineers on highly technical products (AI agents, data platforms)
In-depth understanding of SDLC automation

Job Description

AI Product Lead, Internal Agents | New York (on-site) | $175K–$275K + equity

My client is a fast-growing, well-funded AI company ($100M raised) building one of the first truly AI-native companies at scale. They are hiring two people into a small internal team of builders, former founders, and engineers who embed with teams across the business and build intelligence into how they work.

This is not about making people 10% more productive. The team has already shipped agent teammates that push code to production, systems that keep company knowledge current automatically, and services that diagnose and repair themselves. The goal is zero-human loops that handle entire jobs end-to-end.

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