HCLTech
Website:
hcltech.com
Company:
https://www.linkedin.com/company/hcltech
Seniority: Mid-Senior level
Industries: IT Services and IT Consulting
Job details:
Role — CX AI Product Manager
Level
Individual Contributor / Product Manager
About the Role
You'll own a domain of HCLTech's CX AI product line end-to-end, from understanding how a business process actually works today, to identifying where AI can create meaningful value, to defining what an AI agent should do, to seeing it through build with the shared engineering team, to proving it worked and helping take it to market.
What You'll Own
- 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.
Core Product Management Capabilities
Product Managers are expected to demonstrate strong fundamentals across:
- Customer and user discovery
- Product vision and strategy
- Problem framing and opportunity definition
- Product roadmap management
- Prioritization and trade-off decisions
- MVP definition
- Backlog ownership
- Product requirements and user stories
- Stakeholder management
- Engineering collaboration
- Product analytics and metrics
- Hypothesis and experiment design
- Launch and adoption
- Post-launch measurement and iteration
- Product lifecycle management
- Clear written and verbal communication
Must-Haves
- 7–8 years of relevant Product Management experience, with genuine end-to-end ownership of a product, platform, or meaningful product area — not primarily project or program management
- Demonstrated ability to take ambiguous, messy problems, structure them into clear product opportunities and priorities, and carry them through discovery, build, launch, measurement, and iteration
- Strong customer and problem discovery skills — able to distinguish symptoms from underlying problems and identify which opportunities are worth solving
- Strong product judgment, with experience owning roadmaps and backlogs, defining MVP scope, and making clear prioritization and trade-off decisions
- Experience working closely with engineering teams in an agile product-development environment, including writing product specifications, user stories, and acceptance criteria that teams can execute against
- Experience in one or more CX domains such as Sales, Service, Marketing, Lifecycle Management, Collections, customer journeys, contact-center operations, or process transformation
- Practical understanding of the CX technology stack and how key platforms fit together; product or solution experience with one or more areas such as CRM, CDP, CCaaS, CPaaS, marketing automation, service management, or enterprise data platforms is expected
- Working understanding of AI and agentic AI concepts — including how agents use tools, manage context, are evaluated, and where common failure modes and cost/latency/accuracy trade-offs arise; practical exposure to GenAI or agentic tools is expected
- Comfortable with data, able to define meaningful metrics, evaluate whether something worked, and interrogate data independently; basic SQL or equivalent is useful
- High ownership and influencing ability — able to form and defend a point of view, drive decisions, and influence shared engineering and stakeholder teams without formal authority
- Comfortable with modern Product Management, collaboration, analytics, and GenAI tools — e.g., Jira, Confluence/Notion, Figma, Aha!, Excel/SQL, Power BI/Tableau, and GenAI tools, or equivalent.
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