New opportunity

AI Product Manager

Grazitti Interactive · Panchkula, India

About this role

Grazitti Interactive seeks an AI Product Manager for its SearchUnify team in Panchkula, India. The role owns the vision, roadmap and execution of agentic AI solutions that improve customer support and internal productivity. Working with engineering, data science, design and go-to-market teams, the manager will turn AI capabilities into customer-facing enterprise products.

Responsibilities

Develop AI agents that simplify customer journeys and enable autonomous support workflows. Translate LLM orchestration, RAG pipelines, case summarization and knowledge linking into product features; advance conversational intelligence, self-service automation and proactive support.
Gather customer and prospect feedback, validate hypotheses, prioritize features and analyze competitors and market trends. Define and monitor deflection rate, resolution accuracy, latency and proactive intervention rate; optimize agent accuracy, adaptability and human–AI collaboration.
Align products with GDPR and SOC 2 requirements. Coordinate user acceptance testing, manage sprints and participate in Agile Scrum activities, including planning, backlog grooming, stand-ups and retrospectives.
Partner with Sales, Customer Success and marketing on demos, success stories, positioning, value propositions and go-to-market efforts. Stay current with AI, NLP and agentic AI developments.

Qualifications

3–5 years of product management experience, preferably in AI/ML or enterprise SaaS, and proven experience building and launching customer-facing AI or SaaS products at scale.
Exposure to generative-AI chatbots or customer-experience automation; understanding of agentic AI, LLMs, RAG, conversational AI, prompt engineering, workflow automation and reasoning or planning agents. Familiarity with LangChain, CrewAI, OpenAI and support platforms such as Salesforce, Zendesk, ServiceNow or Freshdesk.
Ability to turn complex support workflows into clear product requirements, communicate and tell product stories effectively, collaborate across teams and use data, experimentation and continuous improvement to guide decisions.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Product ManagementAgentic AIAI AgentsLarge Language ModelsRetrieval-Augmented GenerationConversational AIGenerative AILangChainCrewAIOpenAIPrompt EngineeringWorkflow AutomationLLM OrchestrationNLPSalesforceZendeskServiceNowFreshdeskCustomer Support AutomationProduct RoadmappingProduct RequirementsExperimentationSuccess MetricsUser Acceptance TestingAgile ScrumSprint PlanningGDPRSOC 2CommunicationStorytelling

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Set the SearchUnify agent roadmap
Own the vision, roadmap and execution for agentic AI solutions. Use customer and prospect feedback, hypothesis validation and market analysis to prioritize support and productivity use cases.
Turn AI capabilities into support features
Translate support workflows and capabilities such as LLM orchestration, RAG, case summarization and knowledge linking into clear requirements for customer-facing agents.
Measure and improve support outcomes
Define and track deflection rate, resolution accuracy, latency and proactive intervention rate; use performance data to improve agent accuracy, adaptability and human–AI collaboration.
Coordinate delivery and safeguards
Work with engineering, data science and design; manage sprint activities and UAT while aligning products with GDPR and SOC 2 requirements.
Explain product value to the market
Partner with Sales, Customer Success and marketing on demos, positioning, value propositions and success stories.

Likely challenges to think through

Balancing autonomy with human support
Preparation hypothesis: An agent that handles more work independently may simplify journeys but make incorrect decisions harder to catch. Prepare a workflow example showing where the agent acts, when it hands off to a person and how resolution accuracy would be checked.
Reconciling competing success metrics
Preparation hypothesis: Improving deflection or proactive intervention may not improve resolution accuracy, and additional checks may increase latency. Prepare to explain how you would assess these measures together before prioritizing an agent change.
Making enterprise AI useful within safeguards
Preparation hypothesis: RAG, knowledge linking and case summarization depend on support information, while the role requires GDPR and SOC 2 alignment. Prepare to discuss how you would identify data-handling questions early, turn them into product requirements and test the resulting experience in UAT.

Practice your answers

Technical practice

How would you set a roadmap for agentic support features on SearchUnify?

What to coverSuggested answer focus: explain how you would connect customer support needs and internal productivity goals to a product vision, then use feedback and evidence to prioritize features. Discuss trade-offs between customer value, feasibility and measurable impact.

How would you define requirements for an autonomous support workflow?

What to coverSuggested answer focus: map the customer journey, the tasks an agent may perform and the points where a person should take over. Describe clear requirements for successful resolution, failures and human–AI collaboration.

How would you turn a RAG pipeline and knowledge linking into a support feature?

What to coverSuggested answer focus: explain how relevant knowledge would be found and used to answer a support question, including how you would evaluate whether the answer is grounded and useful. Discuss the trade-off between broader retrieval and irrelevant or inaccurate responses.

How would you evaluate LLM orchestration for case summarization?

What to coverSuggested answer focus: describe the intended user, the inputs and the quality criteria for a useful case summary. Consider prompt design, workflow steps, consistency and when a person should review an output.

How would you measure whether a support agent is succeeding?

What to coverSuggested answer focus: define deflection rate, resolution accuracy, latency and proactive intervention rate in terms of observable outcomes. Explain why a higher deflection rate alone may not indicate better support if accuracy or the customer experience declines.

What would you investigate if an agent became slower or less accurate?

What to coverSuggested answer focus: segment the affected journeys and inspect changes in retrieval, prompts, workflow steps and response timing before proposing a fix. Explain how you would compare improvements against both accuracy and latency rather than optimizing either in isolation.

How would you validate and prioritize a proposed self-service feature?

What to coverSuggested answer focus: formulate a customer-support hypothesis, gather customer and prospect feedback, and specify what evidence would support or weaken it. Compare its expected impact with alternative features and use an experiment or measured release to guide the next decision.

How would GDPR and SOC 2 considerations affect an AI support feature?

What to coverSuggested answer focus: identify where customer or case data enters the workflow and questions to resolve with security and compliance partners before release. Discuss how data handling and access decisions could shape product requirements without assuming a particular implementation.

How would you plan UAT for a conversational support agent?

What to coverSuggested answer focus: propose representative customer journeys, acceptance criteria and tests for incorrect answers, unsuccessful handoffs and other failures. Explain how findings would be triaged into the backlog and affect release readiness.

How would you assess an integration with a customer support platform?

What to coverSuggested answer focus: use a platform named in the posting, such as Zendesk or Salesforce, to describe the support information and actions an agent would need. Discuss workflow fit, access boundaries and how you would validate the integration with users; do not assume a specific integration already exists.

Behavioural practice

Tell me about a time you aligned technical and design partners on a product decision.

What to coverSuggested answer focus: give the situation, your actions with engineering, data science or design, and the result. Explain how you made competing concerns visible and reached a decision.

Describe a time customer feedback changed your product priorities.

What to coverSuggested answer focus: state the original hypothesis and what customers or prospects revealed, then explain the action you took. Close with the result and how you judged whether the change helped.

Tell me about a time you responded to a product quality issue after testing or release.

What to coverSuggested answer focus: describe the situation, your role in diagnosing and coordinating a response, and the result. Include how you communicated the issue and adjusted acceptance criteria or follow-up work.

Describe a time you helped explain a complex product to a go-to-market team.

What to coverSuggested answer focus: identify the audience and the product challenge, then describe how you worked with Sales, Customer Success or marketing. Explain the resulting demo, positioning or message and what you learned from its reception.

Tell me about a time you managed a delivery trade-off during a sprint.

What to coverSuggested answer focus: set out the competing priorities, the action you took with the team during planning or backlog work, and the result. Explain how you kept the decision tied to product goals and communicated any change in scope.

Sharpen your knowledge

Topics to prepare

Agentic support workflows
The posting calls for agents that reason, plan and execute support tasks. Revise by sketching a customer issue from initial question through autonomous steps, escalation and human handoff; explain where LLM orchestration, prompt engineering and workflow automation fit.
RAG and support knowledge
The role translates RAG pipelines, case summarization and knowledge linking into features. Revise by diagramming how an agent retrieves support content, uses it in an answer and handles missing or conflicting information.
Conversational AI ecosystem
The posting names LangChain, CrewAI and OpenAI as examples within the conversational AI ecosystem. Revise by explaining their possible roles in an agent-based product and what a product manager would need to clarify with engineering; familiarity, not use of every tool, is stated.
Support outcomes and trade-offs
The role tracks deflection rate, resolution accuracy, latency and proactive intervention rate. Revise by defining each metric for a hypothetical support journey and explaining why higher deflection would not alone establish a better customer outcome.
Compliance and human–AI collaboration
The posting requires alignment with GDPR and SOC 2 and optimization of human–AI collaboration. Revise by listing questions to raise about support-data handling, agent actions and escalation; treat specific controls as questions to validate, not stated company practices.

Skills to revise

Write agent product requirements
The role turns complex support workflows into clear requirements. Practice writing a brief for an AI agent that summarizes a case and links relevant knowledge, with user journey, boundaries, acceptance criteria and a human-handoff condition.
Prioritize a SearchUnify roadmap
The manager owns vision, roadmap and execution and uses customer feedback to prioritize features. Practice ranking three hypothetical support-agent enhancements using customer need, expected impact, uncertainty and delivery dependencies; label the enhancements as practice scenarios, not announced plans.
Plan measurement and experimentation
The posting emphasizes data-driven decisions and continuous improvement. Practice proposing a test of a self-service change: state a hypothesis, define resolution accuracy and latency checks, and describe what result would prompt iteration.
Run UAT and refine a sprint backlog
The role coordinates UAT and participates in Scrum planning, grooming and retrospectives. Practice drafting UAT scenarios for correct answers, unsupported answers and handoff, then turn one failed scenario into a prioritized backlog item.
Communicate product value across teams
The role partners with engineering, data science, design, Sales and Customer Success on delivery and go-to-market work. Practice a short demo narrative that connects one agent capability to a support problem, an observable outcome and a limitation without claiming results not in evidence.

Your preparation checklist

Prepare a product launch walkthrough
The role calls for experience launching customer-facing AI or SaaS products at scale. If you have a relevant example, prepare a brief walkthrough of the customer problem, your decisions, cross-functional work, launch and measured results; distinguish your contribution from the team’s.
Draft an agentic support design
As a practice exercise, outline one customer-support journey for SearchUnify: the agent’s task, RAG or knowledge-linking flow, case-summary feature, human handoff and failure handling. Label it as a proposed design, not an existing product feature.
Build a discovery-to-roadmap example
Prepare a one-page example showing how customer or prospect feedback would become a validated hypothesis, prioritized requirement and roadmap decision. Include a trade-off between self-service automation and another support need.
Define a measurement and testing plan
For the proposed workflow, specify how you would measure deflection rate, resolution accuracy, latency and proactive intervention rate. Add an experiment and UAT scenarios that could reveal inaccurate answers or poor handoffs.
Rehearse delivery and compliance decisions
Prepare a short account of how you would work with engineering, data science and design through backlog grooming, sprint planning and UAT. Identify questions you would raise about GDPR and SOC 2 alignment rather than assuming a particular implementation.
Prepare a product story for go-to-market partners
Draft a concise demo narrative and value proposition for a support agent, showing the customer problem, the experience and the evidence needed to substantiate a success story for Sales or Customer Success.
Bring targeted questions for the team
Ask which SearchUnify support journeys are priorities, how agent accuracy and human–AI collaboration are evaluated, what customer feedback shapes the roadmap, and where this PM has decision-making ownership.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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