Senior Product Manager - AI Agent
Summary
Senior Product Manager at Veeva Systems in Shanghai drives AI-powered SaaS solutions for life sciences, defining product vision and collaborating with cross-functional teams to enhance commercial operations.
The Role
What You’ll Do
- Define AI Agent use cases, including agent goals, workflows, context, tool usage, human-in-the-loop design, exception handling, and success criteria.
- Work with Engineering, QA, and Customers to deliver high-quality AI Agent capabilities.
- Define and monitor AI product quality metrics such as accuracy, task completion, reliability, user trust, adoption, and business value.
- Use customer feedback and real-world product performance to continuously improve AI Agent capabilities.
- Provide cross-functional leadership to align priorities, drive decisions, and ensure successful product delivery and go-to-market.
Requirements
- 2+ years working on a dedicated AI product team, shipping AI features as a core member.
- Hands-on experience designing, delivering, or continuously improving AI Agent products, including prompt/context engineering, human-in-the-loop workflows, and AI quality evaluation.
- 5+ years of experience in digital products, with demonstrated ownership of complex products or initiatives across the full lifecycle, from discovery and design through delivery, launch, adoption, and ongoing improvement.
- Extensive customer-facing experience, with the ability to independently lead customer discovery and communicate effectively with senior business stakeholders.
- Strong ability to understand complex customer workflows, identify the underlying business problems behind stated needs, and translate ambiguous requirements into clear product direction and solutions.
Nice to Have
- Experience with enterprise SaaS products, configurable platforms, or multi-tenant product environments.
- A technical background in software engineering, computer science, data science, machine learning, solution architecture, or a related field.
- Familiarity with concepts such as large language models, RAG, prompt and context design, tool calling, agent orchestration, APIs, and enterprise data integration.
- Experience in life sciences, pharmaceutical commercial operations, medical affairs, CRM, or another regulated industry.
- Experience designing products that use enterprise data, knowledge bases, unstructured content, or external information sources.
- Familiarity with AI governance, data privacy, information security, compliance, auditability, and responsible AI practices.
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Skills
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