AI Product Manager

I. Role Background

1. We are building an internal AI Enablement operating system with the goal of embedding Large Language Models (LLMs) and Agent capabilities into frontline operations, customer success, sales support, and other key business workflows.
2. The objective is to move beyond simply using AI as a productivity tool and toward reengineering business processes through AI.
3. This role will serve as a key bridge between business teams and AI/technology teams, taking ownership of the planning, implementation, adoption, and continuous iteration of internal AI-powered operations products and workflows.

II. Key Responsibilities

1. AI Operations Enablement Framework
   Support the planning and implementation of the company’s internal AI-enabled operations framework. Identify high-value business scenarios that can be redesigned or significantly improved through AI and Agents, such as content operations, customer support, sales enablement, and data insights. Drive projects through the full lifecycle, from opportunity identification and requirements definition to implementation and launch.
2. Agent Product Design
   Develop product solutions for internal Agent applications based on a solid understanding of Agent architecture and design principles. Break down and orchestrate key capabilities such as Tool Calling, RAG, Skills, and Orchestration, including multi-step and multi-Agent workflows. Define user interaction flows, system capabilities, product boundaries, and appropriate technical trade-offs.
3. Business Needs and Solution Translation
   Work closely with frontline operations teams to understand business pain points, workflows, and operational challenges. Translate ambiguous business needs into clear product requirements and actionable technical solutions, and collaborate with AI, algorithm, and engineering teams to drive implementation.
4. Evaluation and Continuous Improvement
   Build evaluation frameworks and feedback loops for AI-powered operations products, including metrics such as accuracy, adoption rate, efficiency improvement, and ROI. Use data and user feedback to continuously optimize model performance, Prompt and Skill design, workflow configuration, and Agent orchestration.
5. Internal Adoption and AI Enablement
   Promote the adoption of AI tools and AI-powered workflows across internal teams. Organize internal training, establish best practices, and build reusable knowledge and playbooks to improve the organization’s overall AI operations maturity.