AI Product Manager

AI Product Manager ( AI/Agent Products )

Experience: 8-12yrs

Location: Bangalore

Mode of work: Work from Office (4 days a week)

About the Role

We are looking for an AI Product Manager who can own the product vision, roadmap, and user experience of our agentic analytics platform. The solution helps users ask business questions in natural language and get the root cause analysis on top of enterprise grade data. You will define what "good" looks like for each step of the journey, prioritize features with engineering and data, and ensure we ship outcomes that users trust and adopt while respecting the constraints of LLM-based agents and multi-step pipelines.

What You'll Do

• Own the product roadmap: Define and communicate the roadmap for the agentic analytics experience: question expansion, business-question validation, hypothesis generation, data sufficiency, hypothesis testing, and report/chart generation. Balance new capabilities with reliability, performance, and clarity of outputs.
• Turn user and business needs into requirements: Work with users, stakeholders, and support to capture how people ask questions, where they get stuck, and what outputs they need. Write clear PRDs, user stories, and acceptance criteria that engineering and data can implement including edge cases (ambiguous questions, insufficient data, failed tests).
• Define success and drive adoption: Set and track success metrics (e.g. completion rates, time-to-insight, clarification acceptance, report usefulness). Run lightweight discovery (interviews, usage analysis) to improve flows and reduce drop-off.
• Bridge product, engineering, and data: Collaborate with AI/backend engineers on graph design (when to ask for clarification, when to run which subgraph) and with data/analytics on schema, SQL validation, and statistical testing so agent outputs are correct and interpretable. Make tradeoffs explicit (e.g. more steps vs. speed, automation vs. human-in-the-loop).
• Manage scope and prioritization: Prioritize backlog for question expansion, data sufficiency, hypothesis testing, and reporting. Balance technical debt, observability, and UX improvements with new features. Keep stakeholders aligned on timelines and tradeoffs.
• Improve the "agent experience": Define and iterate on clarification prompts, error messages, and report formats so users understand what the system is doing and what to do next. Work with design and eng on streaming, progress, and human-in-the-loop touchpoints.
• Stay close to the stack: Develop enough familiarity with the pipeline (LangGraph flows, LLM calls, tools, state) to have informed conversations with engineering, flag risks, and avoid specifying the impossible or the wasteful.

What We're Looking For

Must-have

• Product management experience (6+ years) with shipped software products; experience with data-heavy or analytics products is a strong plus.
• Ability to write clear requirements: PRDs, user stories, acceptance criteria, and success metrics that engineering and data can execute on.
• Strong collaboration: Comfort working with engineers, data/analytics, and design; able to facilitate tradeoff discussions and keep stakeholders aligned.
• User and outcome focus: Experience with user research, usage data, or support feedback to drive prioritization and iteration.
• Comfort with ambiguity: Ability to scope and prioritize in a space where "perfect" agent behavior is hard to define and iteration is the norm.

Nice-to-have

• Experience with AI/ML or agent products: LLM-based features, chatbots, copilots, or multi-step automated workflows. Understanding of prompt design, tool use, or agent UX (streaming, clarification, errors).
• Familiarity with analytics or data products: BI tools, hypothesis testing, reporting, or data pipelines. Enough to talk to data/eng about schema, validation, and statistical outputs.
• Technical literacy: Ability to read or discuss pipelines (e.g. question validation expansion data check testing report) and ask good questions about feasibility, cost, and latency.
• Experience with enterprise or B2B products: security, compliance, and adoption in larger organizations.