Lead Product Manager, AI Agents

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Lead Product Manager, AI Agents based in the United States. The Lead Product Manager will define and drive the strategy for conversational AI and agent-based experiences serving both consumers and service professionals across a large home services marketplace. As the organization's first dedicated core AI Product Manager, this individual will build the roadmap, establish quality standards, and bring AI capabilities directly into real customer and professional workflows. The role goes beyond traditional chatbots, enabling AI agents to take meaningful actions such as booking, skipping, and rescheduling services while coordinating interactions between marketplace participants. Working as a senior individual contributor, the Lead PM will collaborate closely with Engineering, Applied Science, Design, Data, Customer Service, and Operations. This is a hands-on, high-impact opportunity for an experienced AI product builder who enjoys shipping quickly, solving complex marketplace challenges, and defining the standards for safe and reliable AI experiences. Success will be measured through customer satisfaction, operational efficiency, action accuracy, and improved retention. Accountabilities • Own the AI product roadmap: Define and prioritize the strategy for consumer-facing and service professional AI experiences, determining which capabilities to build and when. • Lead conversational AI experiences: Develop customer-facing AI chat capabilities that answer questions, understand user needs, and complete tasks such as skipping or rescheduling services. • Build AI experiences for service professionals: Own the AI chat experience within the Pros App, helping service providers manage their work and reduce administrative and coordination effort. • Integrate AI into existing workflows: Introduce agentic capabilities directly into consumer and professional product journeys, enabling AI to take action within existing experiences rather than operating exclusively through chat interfaces. • Improve customer-to-professional communication: Shape messaging experiences between homeowners and service professionals, ensuring interactions are effective and support successful service delivery. • Design human-support handoffs: Define when AI should transfer conversations to human support, identify situations requiring escalation, and ensure support agents receive complete conversation context so customers do not need to repeat themselves. • Enable external AI agents: Develop the product capabilities and interaction models that allow third-party AI agents, including ChatGPT and Claude, to communicate with the marketplace, request services, and manage bookings on a customer's behalf while preserving customer control. • Establish AI quality standards: Build and maintain evaluation frameworks (evals) to measure response quality, task completion, and reliability for both consumers and professionals. Define when an AI experience is ready to launch and monitor quality after release. • Develop safety guardrails: Determine which actions AI can execute independently, which require user confirmation, and how the system should respond when an action fails or produces an incorrect result. • Optimize agent decision-making: Design experiences that understand the user's underlying needs, recommend appropriate solutions, and execute the right actions rather than simply responding to literal requests. • Drive measurable product outcomes: Improve AI resolution and containment rates, customer satisfaction for both audiences, and the accuracy of booking, skipping, and rescheduling actions. • Improve professional productivity and retention: Deliver AI capabilities that reduce administrative work for service professionals and contribute to stronger consumer and professional retention. • Lead through cross-functional influence: Share objectives with product leaders responsible for Customer Experience, First Service Experience, and Pro Experience, coordinating priorities and execution across teams. • Build from the ground up: Establish evaluation processes, operational guardrails, and launch-readiness standards in a lean environment with a partially legacy technology stack. • Apply systems thinking: Anticipate the broader effects of product decisions across customers, professionals, pricing, operations, and marketplace performance before implementation. Requirements • At least 6 years of product management experience, including at least 3 years launching AI products for real users, whether in B2C, B2B, or B2B2C environments. • Demonstrated experience bringing AI products into production, including at least one chatbot or conversational AI experience used by real customers or end users. • Strong hands-on experience evaluating AI product performance, including writing evaluation criteria and analyzing conversation transcripts to identify failures, quality gaps, and opportunities for improvement. • Deep understanding of AI agents and tool use, including the transition from generating text to executing actions through real systems. • Ability to design reliable agent experiences and anticipate potential failure scenarios when AI interacts with live products and operational workflows. • Strong product strategy and prioritization skills, with the ability to make evidence-based decisions, establish clear roadmaps, and explain trade-offs. • Data-driven approach to product management, using measurable outcomes and user evidence rather than intuition alone. • Systems-thinking mindset, with the ability to anticipate second- and third-order effects of product decisions across interconnected products, users, and operations. • Experience with consumer-facing products and an understanding of how to deliver intuitive, high-quality user experiences. • Strong cross-functional collaboration and stakeholder