New opportunity

AI Product Delivery Manager

KPMG · Singapore

About this role

KPMG Digital Village in Singapore seeks an AI Product Delivery Manager to lead client-facing AI solutions from identifying use cases through deployment and adoption. The role combines delivery ownership, AI knowledge and client engagement to turn business needs into scalable solutions with measurable outcomes. The posting lists the work type as permanent.

Responsibilities

Own delivery from problem framing and solution roadmaps through development, deployment and adoption. Translate business problems into approaches such as LLM applications, RAG systems and agentic workflows, working with engineering and data teams.
Coordinate data science, engineering, design and business stakeholders across onshore and offshore teams. Manage timelines, budgets, risks, dependencies and multiple workstreams.
Explain technical trade-offs to clients and ensure solutions meet enterprise data, architecture and governance requirements. Track performance, adoption and business impact, then improve solutions using real-world feedback.
Build client relationships and advise on practical AI implementation. Support senior leadership with proposals, solution approaches and new business opportunities.

Qualifications and experience:

Typically 5–8 years of relevant experience in consulting, product delivery or technology roles, including fast-paced, client-facing work with multiple stakeholders. Experience delivering AI or data-driven solutions beyond pilots, from concept through deployment and adoption, is sought.
Understand LLMs, RAG architectures, prompt engineering and emerging agentic patterns. Be familiar with AI deployment on cloud platforms such as Azure or AWS and their constraints and trade-offs.
Bring strong stakeholder management, structured problem-solving, communication and storytelling skills; experience leading distributed, cross-functional teams; commercial awareness in supporting proposals and deals; and proficiency in PowerPoint and Excel. A hands-on approach to problem-solving is expected.

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

Skills for this role

AI product deliveryTechnology consultingClient engagementStakeholder managementLLMsRAGPrompt engineeringAgentic workflowsAzureAWSProject managementRisk managementCross-functional team leadershipBusiness developmentPowerPointExcel

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

Frame client problems and shape AI roadmaps
The role works directly with clients to identify use cases, define solution approaches and turn business needs into practical AI plans.
Own delivery through deployment
KPMG calls for end-to-end ownership across development and deployment, including timelines, budgets, risks and dependencies across multiple workstreams.
Make enterprise-ready AI decisions
The manager translates problems into approaches such as LLM applications, RAG systems or agentic workflows with engineering and data teams, while accounting for scalability, feasibility, cloud constraints and enterprise governance.
Drive adoption and measurable impact
Delivery extends beyond a pilot: the role tracks solution performance, user adoption and business impact, then iterates using real-world feedback.
Advise clients and support new opportunities
The posting calls for clear explanations of technical trade-offs, trusted client relationships and support for senior leadership on proposals and solution approaches.

Likely challenges to think through

Choosing a viable use case under ambiguity
Preparation hypothesis: client opportunities may be broad while data, feasibility and value remain uncertain. Prepare an example of narrowing a business problem into a use case and roadmap, explaining what you would validate before committing to a solution.
Balancing AI capability with enterprise constraints
Preparation hypothesis: a more ambitious LLM or agentic approach may conflict with data, architecture, governance or cloud constraints. Prepare to explain how you would compare approaches and communicate the trade-off to both clients and technical teams.
Protecting delivery while coordinating distributed teams
Preparation hypothesis: parallel workstreams and onshore/offshore handoffs can make dependencies, budgets and timelines harder to control. Prepare a concrete approach to surfacing risks early, assigning decisions and adjusting scope without losing sight of adoption and impact.

Practice your answers

Technical practice

How would you turn a client’s business problem into an AI use case and delivery roadmap?

What to coverSuggested preparation: Explain how you would clarify the problem, users, available data and desired business outcome before choosing an approach. Cover feasibility, dependencies and milestones from development through deployment and adoption.

When would you recommend an LLM application, a RAG system or an agentic workflow?

What to coverSuggested preparation: Compare the need for generated responses, retrieval from source material and multi-step actions. Explain how complexity, reliability and governance constraints would affect the choice.

How would you design and evaluate a RAG-based solution for a client?

What to coverSuggested preparation: Discuss source-data suitability, retrieval quality and how retrieved material supports an answer. Describe how you would test failures and use feedback to improve the solution.

How would you improve prompts in an LLM application?

What to coverSuggested preparation: Explain how you would define the intended task and assess responses against consistent examples. Address the trade-off between improving prompts and changing the underlying data or solution design.

How would you decide whether an agentic workflow is appropriate?

What to coverSuggested preparation: Identify the actions the workflow needs to take and where human review may be appropriate. Explain how you would weigh added capability against complexity, reliability and enterprise governance needs.

What would you assess when planning deployment of an AI solution on Azure or AWS?

What to coverSuggested preparation: Discuss architecture fit, scalability, data requirements and deployment constraints rather than assuming a particular cloud service. Explain how those trade-offs could affect the roadmap and client recommendation.

How would you check that an AI solution aligns with enterprise data, architecture and governance requirements?

What to coverSuggested preparation: Explain how you would establish relevant requirements with client and technical stakeholders before deployment. Describe how you would resolve a conflict between a proposed capability and those requirements.

How would you measure whether a deployed AI solution is succeeding?

What to coverSuggested preparation: Distinguish solution performance, user adoption and business impact, as the posting calls for all three. Explain how real-world feedback would guide iteration after deployment.

How would you manage delivery risks across several AI workstreams?

What to coverSuggested preparation: Describe how you would track timelines, budgets, dependencies and risks across data, engineering, design and business teams. Explain how you would assess and communicate the effect of a change on delivery commitments.

How would you shape a feasible AI solution approach for a client proposal?

What to coverSuggested preparation: Connect the client problem to a proposed approach, delivery roadmap and measurable outcome. Explain how you would communicate assumptions and trade-offs to senior leadership without overstating feasibility.

Behavioural practice

Tell me about a time you brought clarity to an ambiguous client problem.

What to coverSuggested preparation: Give the situation, the actions you took to structure the problem and align stakeholders, and the result. Focus on how that clarity informed a practical solution or next step.

Describe a time you coordinated a distributed, cross-functional delivery team.

What to coverSuggested preparation: Set out the situation, your actions to manage handoffs and dependencies across teams, and the result. Distinguish your contribution from the work of other team members.

Tell me about a time a delivery risk threatened a timeline or budget.

What to coverSuggested preparation: Describe the situation, how you assessed and communicated the risk, what you did, and the result. Be clear about any trade-off you made and what evidence informed it.

Describe a time you explained a technical trade-off to a client or business stakeholder.

What to coverSuggested preparation: Explain the situation, how you made the options and constraints understandable, and the resulting decision. Highlight how you checked that stakeholders understood the implications.

Tell me about a time you helped move a solution from deployment toward adoption.

What to coverSuggested preparation: Describe the situation, your actions to gather user feedback or address adoption barriers, and the result. If applicable, explain how you measured whether the change improved use or business impact.

Sharpen your knowledge

Topics to prepare

Choosing an AI solution pattern
The posting names LLM applications, RAG systems, prompt engineering and agentic workflows. As a revision exercise, take one client problem and compare when each approach would be appropriate, including its limitations.
Cloud deployment trade-offs
The posting expects familiarity with deploying AI solutions on platforms such as Azure or AWS. As a revision exercise, explain how deployment constraints could affect the feasibility, scale and cost of a proposed solution without assuming a particular cloud setup.
Enterprise data, architecture and governance
The role must align solutions with these enterprise requirements. As a revision exercise, list the questions you would ask a client and engineering team before approving a proposed AI solution approach.
Delivery through adoption and impact
The role spans problem framing, development, deployment, adoption and iteration—not just pilots. As a revision exercise, map those stages for a hypothetical use case and identify what evidence would show performance, adoption and business impact.
Commercial solution shaping
The posting includes supporting proposals and defining solution approaches with senior leadership. As a revision exercise, outline how you would explain a proposed AI use case, its trade-offs and expected business outcome in a client proposal.

Skills to revise

Frame a client use case
The role works directly with clients to turn business needs into roadmaps. Practise a short discovery conversation, then write a problem statement, proposed AI approach and questions that must be resolved before delivery.
Build a multi-workstream delivery plan
The posting specifies timelines, budgets, risks and dependencies across workstreams. Practise drafting a plan with milestones, owners, key dependencies and a concise risk log for a hypothetical AI deployment.
Coordinate distributed stakeholders
The role leads data science, engineering, design and business stakeholders across onshore and offshore teams. Practise a status update that assigns decisions and actions, surfaces a blocker and explains its effect on the delivery plan.
Explain technical trade-offs to clients
The posting calls for clear communication between business and technical teams. Practise explaining a choice between two plausible AI approaches in plain language, including feasibility, enterprise constraints and what remains uncertain.
Present outcomes and next steps
The role tracks performance, adoption and impact, and asks for proficiency in PowerPoint and Excel. Practise using a small illustrative dataset in Excel to summarise those measures, then create a brief PowerPoint update recommending an iteration based on feedback.

Your preparation checklist

Build an end-to-end delivery walkthrough
Prepare a concise account of an AI or data-driven solution you helped take from problem framing through deployment and adoption, if available. Show your decisions, collaborators, outcomes and lessons learned; distinguish deployed results from pilot work.
Practice framing a client AI use case
Create a hypothetical client brief and a one-page roadmap that defines the business problem, users, success measures and delivery stages. Explain when an LLM application, RAG system or agentic workflow would—or would not—fit.
Draft a multi-workstream delivery plan
Prepare a sample plan covering data science, engineering, design and business stakeholders, including onshore/offshore coordination. Show how you would track timelines, budget, dependencies and risks.
Explain architecture and governance trade-offs
Sketch a deployable solution and practice explaining its data, architecture, scalability and governance considerations. Compare relevant Azure or AWS constraints without assuming a particular client platform.
Show how you measure adoption and impact
Prepare an example scorecard with solution performance, user adoption and business-impact measures, plus a feedback loop for improving the solution after deployment.
Rehearse a client-facing recommendation
Make a short PowerPoint-style proposal and an Excel-style delivery or impact summary. Practice explaining the solution and its trade-offs clearly to both business and technical stakeholders.
Prepare questions for the employer
Ask how Digital Village defines success after deployment, coordinates distributed teams, handles enterprise governance decisions and involves this role in shaping proposals with senior leadership.

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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