New opportunity

AI Product Manager

E2M Solutions · Ahmedabad, India

About this role

The AI Product Manager in the AI Division leads the development of GenAI-powered products, internal AI tools, automation platforms, and client-facing AI solutions. The role connects business and client needs with technical execution, working with AI engineers, automation engineers, designers, QA, and stakeholders. This is a full-time, onsite role in Ahmedabad.

Responsibilities

Own product vision, strategy, roadmap, and prioritization. Identify business problems and workflows suited to GenAI, automation, and intelligent assistants, balancing value, feasibility, user impact, security, and scalability.
Define requirements for AI assistants, document intelligence, chat interfaces, knowledge systems, workflow automation, and agent-based solutions. Collaborate on LLM selection, prompts, RAG systems, APIs, integrations, and data flows.
Set success metrics covering accuracy, response quality, latency, reliability, adoption, cost, and business impact. Review outputs and establish quality, safety, and usability criteria; use feedback and usage data to improve products after launch.
Address privacy, sensitive data, permissions, hallucinations, data leakage, prompt injection, and misuse. Define human review, fallbacks, and auditability where needed.
Manage backlogs, sprint planning, Agile ceremonies, PRDs, user stories, acceptance criteria, releases, and documentation. Coordinate delivery, present roadmaps and demos, and manage client expectations about AI capabilities and risks.

Required qualifications

4–8 years of total experience, including 2+ years of hands-on AI/ML engineering or AI software development and 2+ years of product management in an Agile environment. Experience with AI, GenAI, SaaS, automation, or technology products and close collaboration with AI or engineering teams is required. Candidates need knowledge of LLMs, agents, prompt engineering and evaluation, RAG, APIs, webhooks, vector databases, embeddings, AI quality evaluation, cloud and deployment concepts, and AI security and governance. Strong product documentation, analytical, prioritization, stakeholder, and client communication skills are required. A bachelor's degree in Computer Science, Engineering, Information Technology, Artificial Intelligence, or a related field is required. Listed tools include Jira, Confluence, Figma, Notion, Postman, SQL, and GenAI tools such as ChatGPT, Claude, or Gemini.

Preferred

Production GenAI product experience; exposure to LLM provider ecosystems, AI assistants, productivity tools, SaaS, cloud platforms, and B2B or AI-first environments; understanding of responsible AI and privacy; an MBA or product management certification. Familiarity with n8n, Zapier, or Make is a plus.

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

Skills for this role

Product managementProduct strategyProduct roadmappingStakeholder managementAgileScrumPRDsUser storiesAcceptance criteriaGenerative AILarge language modelsAI agentsWorkflow automationPrompt engineeringPrompt evaluationRetrieval-augmented generationAPIsWebhooksVector databasesEmbeddingsAI evaluationHuman-in-the-loop workflowsAI governanceData privacyAccess controlData governanceJiraConfluenceFigmaNotion

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

Choose valuable AI opportunities
The role owns AI product vision, roadmaps, and prioritization across GenAI products, internal tools, automation, and client solutions. Preparation: outline how to compare a manual workflow’s business value and user impact with feasibility, security, and scalability.
Turn AI concepts into buildable requirements
The role defines requirements for assistants, document intelligence, knowledge systems, and automation, working with engineers on LLMs, RAG, prompts, APIs, and data flows. Preparation: practice describing a use case through its inputs, integrations, expected behavior, and acceptance criteria.
Build in safety and governance
The role addresses sensitive and client data, permissions, hallucinations, leakage, prompt injection, and misuse, with human review, fallbacks, and auditability where needed. Preparation: map risks to proportionate guardrails for a client-facing AI feature.
Measure quality and improve after launch
The role sets metrics for accuracy, response quality, latency, reliability, adoption, cost, and business impact, then uses output reviews and feedback to improve products. Preparation: distinguish launch acceptance criteria from ongoing measures of product performance.
Coordinate delivery and expectations
The role owns backlogs, sprint planning, PRDs, releases, and documentation while coordinating engineering, design, QA, clients, and business stakeholders. Preparation: show how a roadmap decision becomes a clear delivery plan and a plain-language explanation of AI limitations.

Likely challenges to think through

Value versus technical and security constraints
Likely preparation challenge, not a confirmed employer problem: a promising AI workflow may have uncertain feasibility or unacceptable data exposure. Prepare a decision framework that tests value early, identifies dependencies, and narrows or defers scope when safeguards are insufficient.
Output quality versus speed and cost
Likely preparation challenge: improving response quality may increase latency or expense, while AI outputs can remain inconsistent. Prepare an example evaluation plan with quality thresholds, latency and cost measures, failure cases, and criteria for human review.
Client expectations versus reliable delivery
Likely preparation challenge: stakeholders may want broad AI capabilities before limitations and acceptance criteria are clear. Prepare to explain how to agree on a bounded use case, document what the system will and will not do, and use demos and performance evidence to guide release decisions.

Practice your answers

Technical practice

How would you decide whether a manual workflow needs GenAI, simpler automation, or no new product?

What to coverThe role calls for identifying operational gaps and prioritizing by value, feasibility, user impact, security, and scalability. Suggested preparation: explain how you would compare the options, validate the user need, and make a roadmap decision.

How would you define a RAG-based knowledge assistant at the product level?

What to coverThe posting names knowledge systems, RAG, embeddings, vector databases, data sources, and access control. Suggested preparation: describe the requirements for retrieving relevant content, respecting permissions, handling missing evidence, and evaluating answer quality.

How would you compare LLM and prompt approaches for an AI feature?

What to coverThe role involves collaboration on LLM selection and prompt workflows, with attention to response quality, latency, and cost. Suggested preparation: discuss a representative evaluation set, trade-offs between approaches, and criteria for choosing one.

When would you use an agent rather than a defined automation workflow?

What to coverThe posting includes agent-based solutions, intelligent assistants, and workflow automation. Suggested preparation: compare flexibility with predictability, then explain where tool permissions, human review, and fallback handling would matter.

What requirements would you set for an AI feature that connects to APIs and third-party tools?

What to coverThe role covers APIs, webhooks, integrations, data flows, and secure workflows with third-party services. Suggested preparation: address permitted data flows, access controls, integration failures, and how to test the end-to-end user outcome.

How would you define acceptance criteria and success metrics for an AI assistant?

What to coverThe posting calls for criteria covering quality, safety, and usability, alongside metrics such as accuracy, latency, reliability, adoption, cost, and business impact. Suggested preparation: distinguish pre-release checks from post-launch measures and explain how you would assess variable AI outputs.

How would you reduce hallucination, prompt-injection, and data-leakage risks in an AI product?

What to coverThe role explicitly addresses these risks, plus sensitive data, permissions, misuse, and auditability. Suggested preparation: explain risk-based guardrails, human review and fallback points, and how you would verify that controls work.

How would you turn a client request for document intelligence into a PRD and delivery backlog?

What to coverThe posting calls for requirements for document intelligence and for clear PRDs, user stories, acceptance criteria, and sprint planning. Suggested preparation: show how you would define users, document inputs, expected outputs, edge cases, dependencies, and a testable first release.

What trade-offs would you discuss before launching a GenAI feature?

What to coverThe role emphasizes practical, secure, scalable products and metrics for reliability, latency, and cost. Suggested preparation: discuss how deployment and operational constraints could affect scope, release readiness, and fallback behavior without assuming a particular cloud stack.

How would you decide what to improve after an AI product launches?

What to coverThe posting calls for analyzing user feedback, usage, response quality, and business outcomes to improve workflows, prompts, and prioritization. Suggested preparation: explain how you would identify a problem, distinguish its likely causes, and measure whether a change helped.

Behavioural practice

Tell me about a time you influenced a technical decision without direct authority.

What to coverThe role calls for challenging assumptions and influencing cross-functional teams. Suggested preparation: use a real situation, your actions in working with the team, and the resulting decision or outcome.

Tell me about a time you managed a client's expectations about a product's limitations.

What to coverThe posting emphasizes client communication about AI capabilities, timelines, and risks. Suggested preparation: describe the situation, how you explained the limitation and options, and what resulted.

Tell me about a time competing priorities forced you to change a roadmap or backlog.

What to coverThe role owns prioritization across business value, user impact, feasibility, security, and delivery. Suggested preparation: give a real situation, the criteria and actions you used to make the trade-off, and the outcome.

Tell me about a time you coordinated design, engineering, QA, and stakeholders to deliver a feature.

What to coverThe posting describes cross-functional delivery from requirements through launch. Suggested preparation: explain the situation, your specific coordination and decisions, and the result, including any quality or scope trade-off.

Tell me about a time feedback or product data changed your view of a feature.

What to coverThe role calls for continuous improvement using usage, quality, and business-outcome data. Suggested preparation: describe the original assumption, the evidence and action that followed, and the measurable or observable result.

Sharpen your knowledge

Topics to prepare

AI product strategy and prioritization
The posting calls for owning vision and roadmaps while weighing business value, feasibility, user impact, security, and scalability. To revise, rank three hypothetical AI feature ideas against those factors and explain which you would defer.
LLMs and prompt design
The role requires understanding LLM selection, prompt workflows, and prompt evaluation. To revise, compare two possible LLM approaches for a hypothetical assistant, stating what you would test for response quality, latency, and cost.
RAG and knowledge systems
The posting names RAG, knowledge bases, embeddings, and vector databases at a conceptual/product level. To revise, sketch how a hypothetical document-answering feature retrieves information, and identify where irrelevant retrieval or unsupported answers could arise.
Agents, automation, and integrations
The role covers agent-based solutions, workflow automation, APIs, webhooks, and data flows. To revise, diagram a hypothetical workflow from user request to external system and back, marking permissions, failure points, and any needed human review.
AI success metrics and evaluation
The posting specifies accuracy, response quality, latency, reliability, adoption, cost, and business impact. To revise, choose a hypothetical AI feature and define a small set of metrics, including how feedback or usage data would prompt a product change.
Security and responsible AI
The role explicitly addresses privacy, sensitive data, access control, hallucinations, data leakage, prompt injection, and auditability. To revise, walk through a hypothetical client-data request and identify safeguards, a fallback, and when a person should review the output.

Skills to revise

Write testable AI product requirements
The posting requires detailed PRDs, user stories, and acceptance criteria. As practice, draft a one-page PRD for a hypothetical AI assistant, including its user problem, scope, measurable acceptance criteria, and safety-related failure cases.
Prioritize an Agile backlog
The role owns backlog prioritization, sprint planning, and roadmap execution. As practice, turn the hypothetical PRD into a short ordered backlog and explain the trade-offs behind the first release.
Review outputs against a rubric
The posting calls for reviewing AI outputs and setting quality, safety, and usability criteria. As practice, score several fictional assistant responses against a brief rubric, record failure patterns, and propose one prompt or workflow change.
Specify an integration
The posting names APIs, webhooks, integrations, data flows, and Postman among its tools. As practice, write a sample request-and-response contract for a hypothetical integration, then list validation, permission, and failure-handling checks.
Explain decisions to clients and stakeholders
The role includes demos, roadmap updates, and managing expectations about AI capabilities and risks. As practice, give a two-minute explanation of the hypothetical feature's value, limitations, success metrics, and release trade-offs without relying on technical jargon.

Your preparation checklist

Map the stated requirements to evidence
The posting specifies 4–8 years total experience, 2+ years in hands-on AI/ML engineering or AI software development, 2+ years in Agile product management, and a relevant bachelor’s degree. Prepare a factual timeline and examples for each requirement; do not treat preferred qualifications as required.
Build a GenAI product walkthrough
Prepare one project walkthrough showing the problem, users, your decisions, collaboration with engineers and stakeholders, launch or delivery outcome, and lessons learned. Use an internal tool, automation platform, or client-facing solution if you have a relevant example.
Practice a roadmap prioritization exercise
Draft a one-page roadmap for an AI assistant or workflow automation use case. Explain how you would compare business value, feasibility, user impact, security, scalability, and cost—the trade-offs named in the role.
Draft a sample PRD and technical sketch
Prepare user stories, acceptance criteria, and a simple data-flow diagram for a document intelligence or knowledge-system feature. Be ready to discuss plausible choices involving LLMs, prompts, RAG, embeddings, APIs, and integrations without presenting the sketch as an E2M project.
Define evaluation and safety criteria
Create a compact scorecard for accuracy, response quality, latency, reliability, adoption, cost, and business impact. Add test cases for hallucinations, prompt injection, sensitive-data exposure, permissions, human review, fallbacks, and auditability where appropriate.
Show how you would deliver and improve it
Prepare a sample prioritized backlog, sprint-ready story, release checklist, and post-launch feedback loop. The posting calls for Agile delivery, coordination with design and QA, and using usage data and AI output quality to guide improvements.
Rehearse a client-facing explanation
Prepare a short product update or demo narrative that explains the proposed value, limitations, risks, and next decision in non-technical language. Practice responding to a request that exceeds current AI reliability or security constraints.
Prepare questions for the employer
Ask which AI product types are the immediate priority, how product and engineering share decisions on evaluation and guardrails, what data and client constraints matter most, and which outcomes would define success for this role.

Availability last checked: 5 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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