Product Manager Expert

About the Role

Key Responsibilities

1. End-to-End AI Product Lifecycle ManagementOwn the full product lifecycle of AI applications — including intelligent chatbots, AI assistants, recommendation systems, and automated workflows — from requirements definition and product design through to launch and delivery. Produce high-quality PRDs, interaction flow diagrams, and acceptance criteria while driving cross-functional teams to execute efficiently.
2. LLM / Agent Capability ProductizationDeeply engage in Agent product design, including task planning workflows, tool-calling strategies (Function Calling / MCP), context management mechanisms, multi-agent orchestration logic, and graceful failure/fallback handling. Translate underlying model capabilities into meaningful, perceivable user value.
3. AI Evaluation Framework & Quality StandardsBuild Harness evaluation frameworks for AI products — designing test scenario sets, evaluation metric systems (accuracy, hallucination rate, task completion rate, etc.), and regression test case libraries. Independently assess AI capability boundaries and drive continuous model iteration and product quality improvement.
4. Conversational Product Design & Knowledge EngineeringLead platform architecture planning for intelligent customer service and conversational assistant products, covering knowledge base structure design, dialogue flow and SOP design, corpus annotation and training strategy, intelligent reply generation, and case summarization. Leverage large-scale data analysis to continuously optimize conversation quality and issue resolution rates.
5. Personalization Engines & Generative UI ProductsCollaborate deeply with ML and data teams to design recommendation strategies that deliver truly personalized, individualized user experiences. Define generative UI product requirements and drive growth in core business metrics including CTR, conversion rate, and user retention. Establish comprehensive attribution analysis frameworks for recommendation effectiveness.
6. AI Capability Integration with Business ScenariosDevelop a thorough understanding of business goals and user pain points. Proactively identify opportunities to apply AI technologies — including RAG, Prompt Engineering, and multimodal capabilities — to concrete use cases. Define clear product evolution roadmaps that balance technical feasibility, user experience, and commercial value.
7. Cross-Functional Collaboration & AI-Native Product CultureAct as the product Owner in close collaboration with algorithm, engineering, data, design, operations, and business teams. Maintain a strong sense of ownership in a fast-moving AI-native environment, continuously track industry developments, and rapidly translate emerging AI capabilities into product innovation opportunities.

Requirements

1. Product Experience: 3+ years of product management experience, with at least 1 year of direct hands-on experience delivering AI application products, AIGC products, or Agent products. Must have fully shipped at least one AI-related feature module or standalone product.
2. AI Technical Understanding: Deep understanding of the productization of mainstream AI technologies, including but not limited to: LLM Prompt Engineering, RAG (Retrieval-Augmented Generation) architecture, Agent fundamentals (tool calling / task planning / context window management), and conversational system design. Ability to independently evaluate the product feasibility of technical proposals.
3. Evaluation & Data Capabilities: Proven ability to design AI product evaluation frameworks — independently defining evaluation metrics, building test case sets, and tracking product performance through data analysis. Familiarity with A/B experiment design and a strong data-driven decision-making mindset.
4. Documentation & Communication Skills: Ability to write structured, logically rigorous PRDs, technical requirement documents, and flow diagrams. Excellent cross-functional communication skills with the ability to accurately bridge information between technical and business teams, eliminating information gaps.
5. User Insight & Product Thinking: Sharp ability to translate ambiguous user needs into clearly defined product requirements. Skilled at finding the right product balance between AI capability boundaries and user expectations — avoiding both overpromising and underutilizing model capabilities.
6. Learning Agility & Ownership Mindset: Sustained enthusiasm and capacity for continuous learning in a rapidly evolving AI landscape. Strong sense of ownership — able to proactively define problems, drive decisions, and take accountability for outcomes in highly ambiguous environments.

Nice to Have

• Familiarity with cutting-edge AI engineering practices including MCP (Model Context Protocol), Function Calling, and multi-agent orchestration frameworks (e.g., LangChain / AutoGen / CrewAI), enabling deep technical dialogue with engineering teams.
• Experience with AI Harness / Eval / Benchmark platform product design or usage, with involvement in establishing and maintaining LLM evaluation systems.
• Background in Fintech / e-commerce / SaaS industries, with an understanding of the unique product design requirements for AI in high-concurrency, compliance-sensitive environments.
• Bilingual fluency in Chinese and English (spoken and written), with the ability to independently drive product collaboration and cross-cultural communication within international teams.