Senior Product Manager

4 years

0 Lacs

Posted:1 hour ago| Platform: Linkedin logo

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On-site

Job Type

Full Time

Job Description

Role: AI Product Manager / Senior Product Manager

Experience: 4+ Years

Location: Bangalore, India - WFO


About Us

Online astrology today is stuck in a binary, chat-based format. MyNaksh is expanding the market by creating a daily-use, personalized astrology experience that builds trust first. We combine Vedic wisdom, modern product design, and AI-driven personalization to deliver guidance people actually use every day. Our mobile-first platform is built for Bharat, balancing cultural authenticity with the speed, polish, and scale of world-class consumer tech.


Role Overview

You are the end-to-end Product owner for MyNaksh. You set product strategy, own the roadmap, and lead a cross-functional squad across engineering, design, data, growth, CX, and content to ship outcomes across every user surface. You translate business goals into clear bets, align decision records, define success metrics, and run disciplined evaluations and experiments. You are accountable for safety, cultural sensitivity, reliability, latency, and cost. You ensure every launch is instrumented, reversible, and improves the MyLife, Horoscope, Panchang, community, and consultation experiences in measurable ways.


Responsibilities:

● Cross-functional ownership. Lead a durable squad with engineering, design & data to ship outcomes. Set clear owners, decision records, and review cadence. Unblock teams quickly and protect focus.

● Write detailed product specifications that allow for prioritization and efficient development including use cases and mocks, guardrails, eval plan, latency and cost budgets, and kill criteria. Drive design, engineering, and AI research to hit dates and gates.

● Experimentation. Run disciplined A/Bs with power analysis, pre-declared success metrics, and sequential testing where needed. Balance speed with risk through staged rollouts.

● Prioritization: Build and maintain a clear product roadmap, prioritizing based on user demand, usage patterns, adoption metrics, and business goals.

● India-first UX. Ship multilingual, code-mixed experiences across English, Hindi, and at least one South Indian language. Add transliteration options and cultural review checkpoints before launch.

● Agent design and orchestration. Define and ship multi-agent personalities with clear roles, shared memory, and tool use. Own function-calling, retrieval, and persona consistency across sessions.

● Cross-module state. Define event schemas and an identity graph so agents learn from user behavior across modules. Partner with data and backend to make that state queryable in real time.

● User-Centric Mindset: Continuously gather user feedback, identify pain points, and convert insights into product solutions.

● Compliance and transparency. Implement privacy, consent, retention windows, content disclaimers, and in-product appeal flows aligned to Indian norms. Must-have experience

● 4+ years in product management with 1+ in AI products that shipped to consumers at scale. Ability to balance speed vs. quality while scaling early-stage products.

● Proven delivery of an agentic or LLM product that used retrieval, tool use, and memory, with measurable impact on engagement or revenue

● Proficiency in writing PRDs, user stories, prompts, and technical product documentation. Understanding of LLM behavior, prompt engineering, and AI workflows.

● Comfort with complex technology, analytics, APIs, operating models and experimentation: funnels, retention, cohorting, North Star metrics, power analysis, sequential testing, and launch gatekeeping

● Multilingual consumer product experience in India or comparable markets with code-mixing and transliteration Nice to have

● Background working with agent frameworks, vector stores, and orchestration libraries

● Experience with cultural or faith-sensitive content at scale

● Exposure to reinforcement learning from human or AI feedback and dataset design

● Basic literacy in data engineering constraints, tokenization effects, and ranking

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