AI Product Manager

Job Descriptions：

The AI Product Manager plays a key role in driving the success of AI-powered products by defining vision, strategy, and requirements, collaborating with cross-functional teams, and overseeing the product lifecycle from ideation to launch. This role requires a strong understanding of AI technologies and the ability to deliver consumer or enterprise products aligned with business goals.

Responsibilities：

• Lead end-to-end product planning and execution for GenAI and Agent-based SaaS applications
• Translate complex business workflows into scalable AI agent architectures and task decomposition pipelines
• Design and optimize AI workflows using orchestration platforms such as Dify, Coze, LangChain, LangGraph, n8n, or similar frameworks
• Build and validate multilingual AI agent demos and rapid prototypes for real-world operational scenarios
• Collaborate closely with engineering, AI/ML, data, and operations teams to ensure stable production deployment
• Define evaluation frameworks for AI quality, including hallucination detection, accuracy benchmarking, recall, coverage, and workflow reliability
• Design fallback and degradation strategies for AI systems under latency, token, or model constraints
• Balance response quality, inference cost, and system latency through model routing and workflow optimization
• Continuously monitor advances in LLMs, agents, AI infrastructure, and workflow automation ecosystems
• Drive AI product iteration using user feedback loops, failure analysis, and operational metrics

Requirements：

• 3 years of experience in AI product management, technical product management, or AI solution design
• Experience building or managing GenAI / LLM-powered applications in production environments
• Strong understanding of Agentic workflows, tool calling, prompt engineering, RAG pipelines, and multi-model orchestration
• Familiarity with AI orchestration and automation frameworks such as Dify, Coze, LangChain, LangGraph, CrewAI, AutoGen, or similar platforms
• Ability to independently prototype workflows and collaborate effectively with engineering teams
• Strong understanding of AI system tradeoffs including:
• latency
• hallucination
• token consumption
• evaluation
• fallback mechanisms
• model routing
• human-in-the-loop systems
• Ability to decompose complex business operations into structured AI workflows and deterministic probabilistic system boundaries
• Strong communication and stakeholder management skills across technical and business teams

Preferred Qualifications：

• Experience with multilingual AI applications and international SaaS products
• Familiarity with semantic caching, vector databases, workflow observability, or AI monitoring systems
• Experience designing evaluation datasets, benchmark frameworks, or AI quality metrics
• Experience with AI productivity tools, copilots, automation systems, or enterprise AI deployments
• Basic coding or rapid prototyping capability (Python, scripting, AI-assisted coding, API integrations, etc.)
• Understanding of MLOps, AI deployment workflows, or AI operational monitoring