Solid product fundamentals & product sense owns a product surface end-to-end: discovery, roadmap, prioritization, launch, iteration.

Requirements and stakeholder craft elicits needs from business and technical stakeholders, writes transparent PRDs and acceptance criteria, and manages senior stakeholders and clients with honesty and calibration.

Hands-on with AI tools in their own workflow has used AI-assisted tooling to produce working prototypes, wireframes, or internal tools themselves.

Practical understanding of how AI products behave knows outputs are probabilistic, has thought about how to tell whether a model's answers are actually good, and can talk sensibly about accuracy, cost, latency, and where AI is the wrong tool.

Data-informed decision-making defines metrics before building, runs experiments,



reads a funnel, and connects product decisions to business outcomes.

Valuable-to-have

Design and UX instinct — comfortable producing high-fidelity interactive wireframes and reasoning about interaction patterns.

Domain expertise— financial services, healthcare, energy, manufacturing, public sector.

Depth in AI evaluation — has built or specified test sets and quality benchmarks for generative &agentic; features.

Technical background — engineering, data, or solution-architecture history; enough code fluency to read a PR and challenge an architecture.

📌 AI Product Manager-Senior Associate (Bengaluru)
🏢 PwC
📍 Bengaluru