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Product Management - Senior Product Manager - Pi Agents (SMB)

Paytm - Noida, UP, India - In-office - posted 2026-08-27

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Paytm is India's leading mobile payments and financial services platform, serving 500M+ registered users and 21M+ merchants. Pi Agents is a startup-like team within Paytm building AI agents to help businesses across their full lifecycle: customer acquisition, servicing, retention, upselling, and support. You will own product outcomes for SMB Growth Agents, taking AI-powered agent capabilities from conception to measurable business impact at scale. This is a 0-to-1 product role with direct revenue accountability. Key Responsibilities: - Own the SMB Growth Agents roadmap end-to-end, from problem discovery through GTM and adoption - Define and own outcome metrics: merchant activation, campaign performance, incremental revenue, and retention - Design self-serve agent experiences that SMB merchants trust and use daily, including onboarding, goal setting, autonomous execution, and reporting - Set and enforce a high bar for UX/UI; SMB users demand simplicity, vernacular-friendly interfaces, and mobile-first design with minimal friction - Collaborate with engineering and data science on agent architecture tradeoffs: latency, cost per merchant, guardrails, and evaluations - Run structured experimentation to prove agent-driven lift over merchant self-managed baselines - Partner with sales, ops, and support teams to scale from pilot cohorts to the long tail You will operate in a collaborative, output-driven environment shipping fast with a small, senior team. Requirements: - 4–6 years of core Product Management experience - Strong B2C product experience; experience with small to medium Indian businesses is a plus - Portfolio demonstrating high UX/UI craft and shipped experiences - Data-driven decision-making: fluency in analytics suites (Amplitude, Mixpanel, Clever Tap), SQL, and experiment design - Hands-on experience building AI-native or agentic products is a strong plus; understanding of prompt design, evals, and agent orchestration preferred - Comfort with ambiguity and 0-to-1 environments; bias for shipping and learning weekly

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