Product Manager, AI

Job Summary The Product Manager, AI is responsible for defining and delivering AI-powered product capabilities that create meaningful customer and business value. This role translates customer and business needs into product strategy, roadmaps, and high-impact solutions, partnering closely with engineering, ML/AI engineering, design, and cross-functional stakeholders throughout the product lifecycle. The Product Manager, AI leads product discovery, prioritization, experimentation, and delivery while navigating the unique considerations of machine learning and generative AI, including model performance, reliability, cost, user trust, and responsible deployment. This role uses data, customer insights, and sound product judgment to drive measurable outcomes and continuously improve AI-powered products and experiences.

Major Responsibilities/Activities

• Translate customer and business needs into product strategy, roadmaps, and priorities, identifying opportunities where AI/ML capabilities can create differentiated value.
• Lead product discovery through customer interviews, data analysis, market and competitive research, and experimentation to identify and validate high-impact product opportunities.
• Frame product problems, define hypotheses, and validate solutions through evidence and experimentation, accounting for the probabilistic nature and limitations of AI/ML technologies.
• Prioritize product initiatives and AI opportunities based on customer needs, business value, and technical feasibility, defining and measuring AI-specific product KPIs, evaluation frameworks, and success criteria including quality, accuracy, reliability, latency, cost, and user adoption to create measurable business value.
• Partner closely with engineering, ML/AI engineering, and design to define solutions and deliver high-quality AI-powered products and experiences.
• Own and maintain the product backlog, including clear requirements, user stories, acceptance criteria, and priorities that enable effective team execution.
• Develop clear product documentation, including product requirements, rollout plans, release communications, and documentation of AI model behavior, evaluation criteria, and known limitations.
• Partner with legal, compliance, security, and other appropriate stakeholders to support responsible, secure, and transparent deployment of AI-powered capabilities.
• Maintain stakeholder and customer alignment throughout the product lifecycle, translating technical capabilities, limitations, trade-offs, strategy, and progress into clear business and customer context.
• Maintain knowledge of customers, the business model, product and technical domains, internal tools, and evolving AI/ML capabilities and industry practices to inform product decisions and identify improvement opportunities.
• Present to and facilitate meetings with leaders, communicating strategy, trade-offs, and progress with clarity and confidence.
• Actively participate in Agile/Scrum ceremonies, including backlog refinement, sprint planning, daily stand-ups, retrospectives, and sprint demos.
• Perform other duties as assigned.

Required Education, Skills, & Experience

• Bachelor’s Degree in Business Administration, Product Management, Information Systems, Computer Science, or a related field or equivalent combination of education and relevant work experience.
• 3–5 years of product management experience, including end-to-end delivery of at least one AI/ML-powered product or feature from ideation through launch and post-launch iteration.
• Experience launching new products or capabilities from concept through release and using customer feedback, data, and real-world results to guide continued iteration.
• Experience serving in a product owner capacity within an Agile/Scrum software development environment, including developing user stories, acceptance criteria, and managing product backlogs.
• Working knowledge of ML and LLM systems and concepts, including training and inference, model evaluation, and fine-tuning fundamentals, with an emphasis on evaluating model outputs and driving product outcomes.
• Understanding of AI product considerations and limitations, including model reliability, hallucinations, latency and cost trade-offs, data privacy, and user trust.
• Understanding of data requirements, data quality, retrieval strategies, feedback loops, and governance considerations that influence AI product performance.
• Experience designing…