Agentic AI Product Manager

Responsibilities

• Build and maintain production agentic AI products serving CSO program teams and GEO sales teams
• Work directly with program owners and business stakeholders to identify highest-value use cases for agents
• Design, implement, and iterate on agentic workflows including multi-agent orchestration, tool use, and structured reasoning
• Deploy and customize agents for GEO teams across multiple languages and regional contexts
• Collaborate with the Agent Platform Architects on marketplace deployment, tool adoption, and infrastructure architecture
• Translate ambiguous business problems into well-scoped agent products with clear success metrics
• Iterate rapidly based on field feedback — measure adoption, output quality, and time-to-insight
• Own agent quality — build evaluation frameworks, monitor production accuracy, and drive prompt iteration cycles based on eval results
• Build agents end-to-end and co-develop with business teams — enable them to customize, maintain, and operate agents after initial deployment

Requirements

• 8+ years of technical or product experience shipping production products or features
• Hands-on experience building LLM-powered applications (prompt engineering, RAG, tool use, agent orchestration)
• Experience working directly with non-technical stakeholders to define requirements and iterate on solutions
• Strong communication skills — ability to translate between business needs and technical implementation
• Track record of shipping products that people actually use, with measurable impact

Qualifications

• Experience building multi-agent systems or complex LLM orchestration pipelines
• Familiarity with evaluation frameworks for LLM outputs (accuracy, hallucination detection, quality scoring)
• Full-stack technical skills — ability to build end-to-end from data pipelines through user-facing interfaces
• Experience with multilingual or global-scale deployments
• Background in retail, commerce, training/enablement, or enterprise operations domains
• Experience with MCP (Model Context Protocol) or similar tool-use frameworks
• Comfort with ambiguity and startup-like environments within large organizations
• Experience embedded with business teams to build solutions (solutions engineering, field engineering, or similar)
• Track record of considering how new tools affect existing workflows end-to-end — who feeds data in, who depends on the output, and what breaks when something changes