Job Summary

- A slice of the CX process landscape — for example Lifecycle Management, Sales, Service, Marketing, or Collections — with end-to-end product ownership
- Product vision, strategy, and roadmap for your assigned domain
- Customer and user discovery — understanding how the process actually works, where friction exists, and which problems are worth solving
- The AI agent backlog for your domain — prioritized, scoped, and specified to a level engineering can act on without repeated clarification
- MVP definition and product trade-offs — deciding what needs to be built now, what can wait, and what should not be built at all
- Success metrics for every agent or capability you ship — defined before build, instrumented, and revisited after launch
- Product lifecycle ownership - discovery, validation, requirements, build, launch, adoption, measurement, iteration, and retirement where necessary
- The governance posture for your agents - risk and impact classification, oversight requirements, permissions, auditability, and exception handling built into the product from the start
- The product-market relevance of your domain — ensuring what gets built solves a meaningful client problem, is differentiated, and has a credible path to adoption and commercialization.
- Adoption and value realization for the capabilities you launch — ensuring products are actually used, embedded into workflows, and delivering the intended business outcome.

Key Responsibilities

- Talk to internal stakeholders, domain SMEs, clients, and where possible end users to understand real workflow pain, not just process documentation
- Decompose broad CX processes into specific problems,



user journeys, jobs to be done, and AI-agent opportunities
- Prioritize opportunities based on customer value, business impact, strategic fit, technical feasibility, risk, effort, and commercial potential
- Write product requirements, user stories, functional requirements, and acceptance criteria transparent enough for the engineering team to build against
- Define MVP scope and make trade-offs when engineering capacity is constrained
- Sit in sprint planning, backlog refinement, reviews, and demos with engineering as the domain's product voice - prioritize, unblock, and push back on scope creep
- Get into the details of how your agents behave in production: what breaks, what's slow, what's wrong, what users reject, and why
- Define hypotheses and experiments where appropriate before committing significant build effort
- Instrument and track outcome metrics yourself where possible; don't wait for someone else's dashboard
- Review customer feedback, adoption, agent quality, task success, latency, cost, exception rates, and relevant business outcomes after launch
- Work with architecture and engineering teams to understand integration dependencies across CRM, CDP, CCaaS, CPaaS, marketing platforms, enterprise data, APIs, identity, and permissions
- Work with sales and solution teams to turn product capabilities into clear client propositions, business cases, and ROI narratives
- Keep track of material changes in the CX and agentic AI market that could affect roadmap, differentiation, or positioning

Skill Requirements

Must Have Skill

Product Design

Good to Have Skill

Marketing Analytics & Product Management

Other Requirements

📌 Product Manager/ Product Lead (India)
🏢 HCLTech
📍 India