New opportunity

Product Manager - AI Services

Veralto Global · Bangalore, India

About this role

Esko, part of Veralto, seeks a Product Manager - AI Services to manage the lifecycle of shared AI capabilities used by its product and engineering teams. The role supports Esko’s AI-native Product Intelligence Platform, which brings together product, packaging, regulatory, quality, manufacturing and supply chain data. It is based in Bangalore, with a quarterly visit for two weeks noted in the posting, and reports to the Director, Packaging Intelligence.

Responsibilities

Own and prioritize the AI Services backlog and maintain a roadmap aligned with company strategy, AI transformation objectives and the needs of multiple product teams.
Gather and consolidate cross-functional needs; work with technical and design stakeholders to produce clear, testable requirements approved before development. Assess competing requests, risks and trade-offs.
Connect AI engineering with product, engineering and commercial teams. Help teams build on existing shared services, identify capabilities that need development and launch AI initiatives that deliver customer value.
Communicate progress and priorities, support acceptance testing, handle change requests and drive production adoption. Maintain services as a coherent, reusable foundation while considering evolving needs and backwards compatibility.
Define and track adoption and success metrics to demonstrate impact and guide prioritization. Contribute to AI strategy through customer discussions, events and internal thought leadership.

Requirements

At least 5 years of software product management experience; ownership of a platform, API or shared-services product used by multiple internal teams is preferred.
An undergraduate or master’s degree in computer science or a related discipline, and native or fluent English proficiency.
Experience taking AI-powered capabilities from proof of concept or early ideas through production deployment and scaling. Strong backlog, roadmap and requirements-management skills, plus experience aligning stakeholders across a large, global organization and communicating with technical and business audiences.

Preferred

Experience in packaging graphics or the wider graphic arts industry is strongly preferred.

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

Skills for this role

AI product managementSoftware product managementAI service deploymentAI service scalingPlatform product managementAPI product managementBacklog managementProduct roadmappingRequirements gatheringStakeholder managementCross-functional collaborationAcceptance testingAdoption metricsRisk assessmentWritten communicationVerbal communicationEnglish

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

Build a reusable AI Services foundation
The role owns shared AI capabilities that product and engineering teams can use instead of building one-off solutions. The posting defines success as teams shipping customer-valued AI capabilities on a coherent shared foundation.
Own the backlog and roadmap
Prioritize requests from multiple teams against company strategy and AI transformation objectives. Keep engineering’s backlog clear and communicate roadmap status, priorities, risks and trade-offs to product, engineering and leadership.
Turn cross-functional needs into buildable requirements
Gather and reconcile input from product teams, then work with technical and design stakeholders on clear, well-scoped, testable requirements reviewed before development begins.
Move AI capabilities into production use
Help teams identify existing services and what still needs building, support acceptance testing and change requests, and remove adoption barriers through guidance and documentation. The posting also calls for experience taking AI capabilities from early ideas through production scaling.
Measure outcomes and adjust direction
Define adoption and success metrics, use them to identify risks and reprioritize work, and contribute customer and market insights to Esko’s AI strategy.

Likely challenges to think through

Shared platform versus team deadlines
Preparation hypothesis: individual product teams may want fast, tailored capabilities while AI Services must remain reusable and aligned with central priorities. Prepare a concrete way to compare requests, explain trade-offs and decide when an idea merits a shared service.
Change without breaking existing users
Preparation hypothesis: evolving AI capabilities could create tension between new team needs and backwards compatibility for teams already using a service. Prepare to discuss how you would scope changes, set acceptance criteria and plan adoption without fragmenting the foundation.
Demonstrating value beyond a launch
Preparation hypothesis: a deployed service may not deliver customer value if teams struggle to use it or build duplicate solutions. Prepare an approach to tracking production adoption and downstream outcomes, finding barriers, and feeding the evidence into backlog decisions.

Practice your answers

Technical practice

How would you decide whether an AI capability belongs in a shared service rather than a product-specific solution?

What to coverSuggested answer focus: Compare needs across product teams, likely reuse, customer value and the cost of generalizing the capability. Explain how you would avoid a one-off solution without delaying a team's immediate objective unnecessarily.

How would you prioritize competing requests for the AI Services backlog?

What to coverSuggested answer focus: Weigh company strategy, AI transformation objectives, cross-team demand, expected value, dependencies and risks. Show how you would document trade-offs and give engineering a clear priority order.

How would you turn differing team needs into requirements ready for AI engineering?

What to coverSuggested answer focus: Describe how you would find the common need, define scope and write unambiguous, testable acceptance criteria. Explain how technical and design stakeholders would review the approach before development begins.

How would you take an AI capability from proof of concept to a production service other teams depend on?

What to coverSuggested answer focus: Identify what must be validated before committing to a shared capability, then address production readiness and adoption by consuming teams. Distinguish evidence that a concept works from evidence that it reliably delivers customer value in use.

How would you plan for an AI service to scale across multiple product teams?

What to coverSuggested answer focus: Discuss how you would identify shared versus team-specific needs and coordinate dependencies with AI engineering and consuming teams. Explain the trade-off between expanding the service's reach and keeping it coherent and maintainable.

How would you evolve a shared AI service while preserving backwards compatibility?

What to coverSuggested answer focus: Describe how you would assess the impact of a proposed change on existing consumers and communicate it before release. Weigh emerging needs and technology advances against disruption to teams already using the service.

How would you support acceptance testing and handle a change request before production adoption?

What to coverSuggested answer focus: Tie acceptance testing to the agreed requirements and clarify who needs to assess whether the service meets the intended need. Explain how you would triage a change request against scope, risk, other consumers and roadmap priorities.

How would you measure whether shared AI capabilities are succeeding?

What to coverSuggested answer focus: Consider adoption by product teams alongside evidence that shipped capabilities solve valuable customer problems. Explain how weak adoption or outcomes would change your backlog priorities or reveal barriers needing attention.

How would you help a product team start an AI initiative using existing services?

What to coverSuggested answer focus: Describe how you would establish the team's customer problem, explain available capabilities and documentation, and identify any genuine gaps. Show how you would reduce adoption barriers while keeping requests for new work visible in the shared backlog.

How would you keep an AI Services roadmap useful as customer needs and technology change?

What to coverSuggested answer focus: Connect current status and future objectives to company strategy, customer discussions and evidence from service adoption. Explain how you would communicate changes and trade-offs to engineering, product and leadership without treating the roadmap as a list of promises.

Behavioural practice

Tell me about a time you aligned teams with competing priorities around a shared product.

What to coverSuggested answer focus: Use a real situation to explain the disagreement, the actions you took to surface common needs and trade-offs, and the resulting decision. State the outcome for the teams involved rather than claiming consensus without evidence.

Describe a time you helped another team adopt a capability your team owned.

What to coverSuggested answer focus: Set out the team's starting problem, how you explained the capability or removed an adoption barrier, and what happened afterward. Include what you learned about making a shared service easier to use.

Tell me about a production launch or change request that did not go as planned.

What to coverSuggested answer focus: Explain the situation, your role in testing or triage, the actions you took with affected stakeholders, and the result. Address how the experience changed your approach to readiness or ongoing service management.

Give an example of communicating a difficult product trade-off to technical and business stakeholders.

What to coverSuggested answer focus: Describe the decision context, how you adapted the explanation for each audience, and what action followed. Include the outcome and any remaining disagreement rather than presenting communication alone as the result.

Describe a time customer input changed your product direction.

What to coverSuggested answer focus: Identify the customer insight, how you evaluated it alongside existing priorities, and what you did as a result. Explain the observed outcome and what the example shows about connecting strategy to customer value.

Sharpen your knowledge

Topics to prepare

Shared AI services as a product
The role owns common AI capabilities used by multiple Esko product and engineering teams. Revise how a reusable service differs from a team-specific solution: for a hypothetical request from two teams, explain what should be shared and what should remain with each team.
From AI concept to production
The posting calls for experience taking AI-powered capabilities from early ideas or proofs of concept through deployment and scaling. Review the decisions at each transition; practise explaining when other teams could depend on a capability in production.
Reuse, evolution and compatibility
Esko wants AI Services to remain coherent as needs change, while maintaining backwards compatibility. Revise the trade-off by comparing a hypothetical change that benefits one team with its effects on other teams already using the service.
Adoption and success measures
The role defines metrics to show impact and guide backlog decisions. Revise the distinction between a service being available, teams adopting it, and customers receiving value; propose one measure for each as a preparation exercise, not as prescribed Esko metrics.
Packaging and product-data context
Packaging graphics or graphic arts experience is strongly preferred, and Esko describes a platform unifying product, packaging, regulatory, quality, manufacturing and supply-chain data. As optional preparation, sketch how those data areas might intersect in a packaging workflow; do not assume a specific Esko implementation.

Skills to revise

Write testable requirements
The posting explicitly requires clear, well-scoped requirements reviewed before development. Draft a hypothetical shared-service request with its intended user value, scope, dependencies and acceptance criteria; check that engineering could test each criterion.
Prioritize a multi-team backlog
The role weighs competing requests against strategy and documents risks and trade-offs. Practise ranking three hypothetical requests from different product teams, then write a short rationale for what enters the backlog first and what is deferred.
Communicate a service roadmap
Esko expects roadmap and priority communication across engineering, product and leadership. Create a brief hypothetical roadmap showing current work and future objectives, then explain the same trade-off in technical and business language.
Facilitate cross-functional alignment
The role gathers needs from varied stakeholders and bridges AI engineering with product and commercial teams. Rehearse a discussion in which two teams want different versions of a capability: clarify their underlying needs, identify common ground and record the unresolved decision.
Support acceptance and production adoption
The posting includes acceptance testing, change requests, service documentation and removing adoption barriers. For a hypothetical release, prepare an acceptance checklist and a short guide explaining what exists, how a product team can get started and how it can raise a change request.

Your preparation checklist

Map the role to Esko’s platform
Prepare a one-page summary of how shared AI Services could support product teams using Esko’s Product Intelligence Platform. Distinguish the posting’s stated platform vision from your own illustrative use cases.
Build a production AI walkthrough
If you have a relevant example, outline the path from early idea or proof of concept to deployment and scale: customer problem, your decisions, dependencies, setbacks and measured results. The posting specifically seeks this experience.
Practice prioritizing a shared-services backlog
For a hypothetical set of requests from several product teams, draft a short roadmap. Show how you would weigh reuse, customer value, strategy, risks and trade-offs rather than favoring one team’s timeline.
Write a testable requirement
Draft a sample AI service requirement with scope, acceptance criteria and questions for engineering and design. Include how stakeholders would review it before development and handle a later change request.
Plan for adoption and ongoing success
Create a sample scorecard covering service adoption and customer-value indicators. Add a brief plan for documentation, acceptance testing, production rollout and backwards compatibility; these are responsibilities named in the posting.
Prepare cross-functional evidence
Select examples, where available, of aligning technical and business stakeholders across teams. Note how you communicated priorities and resolved competing needs; also have factual evidence ready for the stated experience, degree and English requirements.
Draft targeted employer questions
Ask which AI capabilities are shared today, where product teams face adoption barriers, how success is measured, and what packaging-graphics knowledge matters most. Clarify the posting’s Bangalore location and quarterly two-week visit wording rather than assuming a work arrangement.

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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