New opportunity

Product Manager – AI-First Products

Deltek, Inc · Bangalore, India

About this role

Deltek seeks a Product Manager for AI-first products serving project-based businesses. The role focuses on using AI to improve product-management productivity, create better user experiences, and identify new product or business opportunities.

Responsibilities

Define the product vision, roadmap, and business outcomes; translate customer problems into opportunities where AI can deliver measurable value.
Evaluate build, buy, partner, and AI-enabled alternatives. Set requirements, success metrics, and acceptance criteria, and prioritize work by customer and business outcomes.
Collaborate with Engineering, Design, Data Science, and go-to-market teams. Measure adoption, retention, and value realization.
Follow developments in AI models, agents, and automation platforms; promote experimentation and establish frameworks for responsible AI use and evaluation.

Qualifications and application:

Graduate with relevant experience and a commitment to delivering high-quality products. Strong analytical and problem-solving skills, experience working with technical teams, and experience with AI-enabled products are required. Familiarity with LLMs, AI agents, RAG architectures, workflow automation, or machine learning concepts is sought. The posting mentions 4+ years of product-management experience but explicitly says this is not mandatory.
Applicants are asked to describe the AI tools they regularly use, share any AI-led development work and project links, show how they would use AI to respond to the application, and provide a favorite prompt.

This is a full-time remote role listed in Bangalore, India, with 10% travel.

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

Skills for this role

Product ManagementProduct StrategyAI-enabled productsLarge Language Models (LLMs)AI agentsRetrieval-Augmented Generation (RAG)Workflow automationMachine LearningResponsible AIAI evaluationProduct roadmappingAnalytical problem-solving

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

Find measurable AI opportunities
The role translates customer problems into opportunities where AI creates measurable value for project-based businesses. Prepare to explain how you would establish the customer problem and define the outcome before proposing an AI solution.
Set strategy and make investment choices
The posting assigns ownership of product vision, roadmap, and business outcomes, including evaluation of build, buy, partner, and AI-enabled alternatives. Prepare to show how customer and business outcomes would guide those choices.
Deliver and measure product value
The PM works with Engineering, Design, Data Science, and go-to-market teams to set requirements, success metrics, and acceptance criteria. Adoption, retention, and value realization are named measures; prepare an example of connecting delivery decisions to such outcomes.
Lead practical AI experimentation
The role calls for keeping current with models, agents, and automation platforms, finding internal and external uses, and establishing responsible-AI evaluation frameworks. The application also asks for regular AI-tool use and AI-led work, so prepare specific, evidence-backed examples.

Likely challenges to think through

Distinguishing value from novelty
A likely difficulty is deciding when AI genuinely improves a user experience or workflow rather than merely adding a feature. Prepare a decision framework that compares an AI approach with a simpler alternative using customer evidence and measurable outcomes.
Balancing speed with responsible evaluation
Rapid experimentation may compete with the need to evaluate AI behavior responsibly. Prepare to discuss how you would define acceptance criteria, test limitations, and decide whether to expand, revise, or stop an experiment.
Connecting adoption to realized value
Usage alone may not establish that an AI-first product helps customers. Prepare to explain how you would interpret adoption alongside retention and value realization, then use the findings to reprioritize the roadmap.

Practice your answers

Technical practice

How would you turn a customer problem in a project-based business into an AI product opportunity?

What to coverPreparation suggestion: Explain how you would establish the problem, compare an AI approach with a conventional one, and define measurable customer value. Distinguish an improved user experience from merely adding an AI feature.

Where could AI improve product-management productivity?

What to coverPreparation suggestion: Choose a product-management workflow, describe where AI could help, and explain what a person would still need to verify. Address how you would measure time saved without sacrificing decision quality.

How would you prioritize an AI-first product roadmap?

What to coverPreparation suggestion: Relate proposed work to the product vision, customer problems, and business outcomes named in the posting. Explain how evidence, expected value, and uncertainty would affect sequencing.

How would you evaluate build, buy, partner, and AI-enabled alternatives?

What to coverPreparation suggestion: Compare the alternatives against the customer need and desired outcome rather than assuming AI is best. Discuss trade-offs such as control, integration effort, ongoing evaluation, and speed to value.

When would you consider an LLM, a RAG architecture, or an AI agent?

What to coverPreparation suggestion: Explain the different purposes of generation, retrieval-grounded responses, and multi-step action, including where simpler software may suffice. Describe the evidence you would seek before selecting an approach; the posting does not prescribe an architecture.

How would you assess workflow automation versus an AI-enabled workflow?

What to coverPreparation suggestion: Identify which steps are predictable enough for conventional automation and which might benefit from AI. Address reliability, oversight, and whether the proposed change improves the user's outcome.

How would you write requirements and acceptance criteria for an AI-enabled feature?

What to coverPreparation suggestion: State the user problem, intended behavior, success metrics, and conditions for an acceptable result. Account for variable outputs and how the team would handle results that do not meet the criteria.

What would a responsible-AI evaluation framework cover?

What to coverPreparation suggestion: Describe how you would test usefulness and failure cases, decide when human review is needed, and revisit performance after release. Keep the framework tied to the product's actual use rather than assuming a particular company policy.

How would you run an AI product experiment before committing to a roadmap?

What to coverPreparation suggestion: Define the hypothesis, the smallest useful test, and the result that would justify continuing, changing direction, or stopping. Explain how you would learn quickly while avoiding conclusions from a narrow demonstration.

How would you measure adoption, retention, and value realization?

What to coverPreparation suggestion: Distinguish initial use from sustained use and from a meaningful customer or business outcome. Explain how you would investigate a case where adoption rises but realized value does not.

Behavioural practice

Tell me about a time you translated a customer problem into a product opportunity.

What to coverPreparation suggestion: Use a real situation, the research or analysis you performed, your decision, and the result. If AI was involved, explain why it was appropriate rather than treating its use as the achievement.

Describe a time you aligned technical and nontechnical teams on a product decision.

What to coverPreparation suggestion: Give a real situation involving differing perspectives, the actions you took to clarify requirements or trade-offs, and the result. Make your own contribution clear without claiming work done by others.

Tell me about an experiment with an emerging technology that changed your thinking.

What to coverPreparation suggestion: Describe the real question you tested, what you did, what the results showed, and how your next decision changed. An inconclusive or unsuccessful experiment can be useful if you explain the learning.

Describe a time you had to prioritize competing customer and business outcomes.

What to coverPreparation suggestion: Set out the real situation, the evidence and trade-offs you considered, the action you took, and the result. Explain what you would reconsider with the benefit of hindsight.

Tell me about a time you identified a risk in an AI-enabled product or workflow.

What to coverPreparation suggestion: If you have a real example, describe the risk, your evaluation or mitigation, and the result using situation, action, and result. If you do not, do not invent one; prepare to discuss how you would approach such a situation.

Sharpen your knowledge

Topics to prepare

AI-first product opportunities
The role asks whether AI can improve PM productivity, user experience, or create new opportunities for project-based businesses. Revise these three questions by taking a hypothetical customer problem and explaining when AI would deliver measurable value—and when a conventional solution would be better.
Product strategy and alternative selection
The posting calls for a product vision, roadmap, business outcomes, and build, buy, partner, and AI-enabled comparisons. Practice comparing those alternatives for one hypothetical opportunity, stating the assumptions and customer outcome behind your choice.
AI-enabled product concepts
Familiarity with LLMs, AI agents, RAG architectures, workflow automation, or machine-learning concepts is sought. Revise what each can and cannot do, then explain which, if any, fits a hypothetical product problem. This is a preparation exercise, not a stated requirement to use every approach.
Adoption and value measurement
The role measures adoption, retention, and value realization. Revise how these differ; for a hypothetical AI feature, propose one measure for each and explain what result would prompt reconsideration.
Responsible AI and evaluation
The posting calls for frameworks for responsible AI use and evaluation. Revise how to assess output quality, failure cases, and appropriate human oversight; apply those questions to a hypothetical AI-enabled user experience.

Skills to revise

Frame and prioritize customer problems
Practice writing a short problem statement for a hypothetical project-based business customer, then rank two possible solutions by customer and business outcomes. This mirrors the role’s problem translation and prioritization responsibilities.
Write testable product requirements
Draft a brief requirement, success metric, and acceptance criteria for a hypothetical AI-enabled feature. Check that Engineering, Design, and Data Science could use them to evaluate delivery, as the posting requires.
Plan a rapid experiment
Outline a small test of a hypothetical AI opportunity: assumption, evaluation method, decision threshold, and next decision. This practices the role’s emphasis on experimentation and rapid learning without implying a prescribed Deltek process.
Explain cross-functional trade-offs
Rehearse a concise discussion of one product trade-off from Engineering, Design, Data Science, and go-to-market perspectives. The posting explicitly names collaboration with these teams.
Prepare concrete AI-workflow examples
The application asks for regularly used AI tools and how they are used, any AI-led development work, an AI-assisted application workflow, and a favorite prompt. Practice describing only examples and evidence you actually have; do not create a project or claim tool use solely for the application.

Your preparation checklist

Prepare an AI product walkthrough
Choose a real AI-enabled product or development effort you can discuss. Document the customer problem, your decisions, collaboration with technical teams, outcome, and what you learned; include a project link or repository only if available.
Practice an AI-first product case
As an interview exercise, choose a problem faced by a project-based business and draft a one-page vision and roadmap. Explain whether AI improves productivity, changes the user experience, or creates a new opportunity—and how you would test its value.
Compare solution approaches
For the same case, make a brief build, buy, partner, and AI-enabled alternatives table. State assumptions, trade-offs, and a recommendation tied to customer and business outcomes.
Draft a delivery and evaluation plan
Write sample requirements, acceptance criteria, and success metrics for your proposed solution. Include how you would assess adoption, retention, value realization, and responsible AI use with Engineering, Design, Data Science, and go-to-market partners.
Complete the requested AI application responses
Prepare the four items the posting requests: AI tools you regularly use and how, any AI-led development work with a summary and available links, the prompts or workflow you would use to respond to this application, and a favorite prompt. Keep claims supported by your own evidence.
Bring focused questions for Deltek
Prepare questions about customer problems across the project lifecycle, how the team identifies measurable AI opportunities, and how it evaluates experiments and responsible AI use. These topics reflect the posting; the questions are for learning how the team approaches them.

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

KEEP EXPLORING

Similar AI jobs

Related skills and specializations in India. Matched to this role, not your profile.

Explore more jobs

Finding similar opportunities…