New opportunity

AI Product Manager

Surbana Jurong · Singapore

About this role

As part of the Central AI-Core team at Surbana Jurong, you will drive the product strategy and delivery of AI-enabled products across the group's business units. The role focuses on transforming workflow friction in design, tendering, construction management, and document intelligence into adopted AI products. Key Responsibilities: Identify high-value AI use cases and define product vision, roadmap, and success metrics. Translate ambiguous workflow pain points into clear product requirements, user stories, and release scopes. Partner with AI engineers, data teams, designers, and business stakeholders to manage products from concept to production. Balance experimentation with delivery discipline, including decisions on automation and human-in-the-loop processes. Communicate AI risks and business value to stakeholders. Embed model evaluation, explainability, safeguards, and governance into product design. Define and monitor KPIs for adoption, cycle-time reduction, and user satisfaction. Required Qualifications: A track record of delivering digital or workflow products end-to-end. Demonstrated fluency in applied AI, including generative AI, retrieval, workflow automation, and intelligent decision-support patterns. Strong structured problem-solving and stakeholder management skills. Ability to translate business workflows into execution-ready product artifacts for Agile teams. Preferred Qualifications: Hands-on experience in AEC, infrastructure, real estate, or other complex, workflow-heavy business lines. Exposure to enterprise AI products, AI governance, model evaluation, or transformation programmes.

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

Skills for this role

Product StrategyGenerative AIRetrievalWorkflow AutomationIntelligent Decision-SupportStakeholder ManagementAgileAI GovernanceModel EvaluationProduct RoadmapKPI Monitoring

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

Translating Workflow Friction into AI Products
The core mandate is to identify high-value use cases within design, tendering, and construction management. You must bridge the gap between ambiguous operational pain points and technical requirements, ensuring that the resulting AI solutions are not just functional but actually adopted by business units.
Balancing Experimentation with Delivery Discipline
You are expected to manage the product lifecycle from concept to production. This requires making strategic decisions on what to automate versus keeping a human-in-the-loop, and knowing when to abandon non-viable ideas to maintain focus on high-impact delivery.
Embedding AI Governance and Quality Assurance
The role requires integrating model evaluation, explainability, and safety safeguards directly into the product design. You must ensure that AI outputs are reliable and governed, which is critical for complex, high-stakes infrastructure and engineering environments.

Likely challenges to think through

Navigating Complex Stakeholder Environments
Surbana Jurong operates across diverse business units with specific project realities. You will face the challenge of aligning executive expectations with operational needs. Prepare by demonstrating a consulting-led approach to problem-solving and showing how you communicate technical AI risks to non-technical stakeholders.
Defining Success in Ambiguous Domains
Because the role involves transforming complex, workflow-heavy processes, success metrics may not be immediately obvious. You must be prepared to define and monitor KPIs like cycle-time reduction and quality uplift, and iterate based on real-world user feedback post-launch.

Practice your answers

Technical practice

Defining AI Product Success Metrics

What to coverExplain how you would define KPIs for an AI tool in a construction or design workflow. Focus on balancing quantitative metrics like cycle-time reduction with qualitative measures like user satisfaction and quality uplift.

Human-in-the-Loop Design

What to coverDescribe your framework for deciding which parts of a complex workflow should be fully automated versus requiring human intervention. Discuss the trade-offs between efficiency, risk, and model reliability.

AI Governance and Safeguards

What to coverHow do you integrate model evaluation, explainability, and safety protocols into the product development lifecycle? Address how you ensure these governance requirements do not hinder delivery speed.

Retrieval-Augmented Generation (RAG) in Enterprise

What to coverDiscuss the challenges of implementing retrieval patterns for document intelligence in a large organization. Focus on data privacy, accuracy, and how to maintain source integrity when providing AI-generated answers.

Translating Ambiguity into Agile Artifacts

What to coverDescribe your process for taking a vague business pain point, such as 'tendering friction,' and breaking it down into actionable user stories and release scopes for an engineering team.

AI Model Evaluation Strategies

What to coverWhat methods do you use to evaluate the performance of an AI model post-launch? Explain how you iterate on the product based on real-world usage data versus initial performance benchmarks.

Workflow Automation Patterns

What to coverProvide an example of how you have applied intelligent decision-support patterns to improve a complex business process. Focus on how the AI system interacts with existing legacy workflows.

Managing AI Risks

What to coverHow do you communicate technical AI risks, such as hallucinations or bias, to non-technical executive stakeholders? Suggest a strategy for balancing transparency about these risks with the need to drive product adoption.

Behavioural practice

Managing Cross-Functional Stakeholders

What to coverDescribe a situation where you had to align conflicting priorities between AI engineers, designers, and business unit leaders. How did you reach a consensus while maintaining the product roadmap?

Navigating Ambiguous Environments

What to coverShare an example of a time you were tasked with solving a problem with little initial direction. How did you structure your approach to identify the core issue and define a clear path forward?

Driving Product Adoption

What to coverTell me about a time you launched a digital product that faced initial resistance from users. What steps did you take to understand the friction and increase adoption rates?

Balancing Experimentation and Delivery

What to coverDescribe a project where you had to decide whether to continue investing in an experimental AI feature or pivot to a more reliable solution. What factors influenced your decision-making process?

Sharpen your knowledge

Topics to prepare

AI Governance and Risk Management
The role requires embedding safeguards, explainability, and governance into product design. Exercise: Create a risk assessment framework for an AI tool in a high-stakes environment like construction, identifying potential failure modes and mitigation strategies for model bias or hallucinations.
AEC Industry Workflow Integration
The position focuses on design, tendering, and construction management. Exercise: Map a standard construction tendering process and identify three specific friction points where document intelligence or automated decision-support could realistically reduce cycle time.
Human-in-the-Loop Product Design
You must balance automation with human oversight. Exercise: Define a decision matrix for a hypothetical AI tool that determines when an automated output is sufficient and when it must be routed to a human expert for validation.

Skills to revise

Translating Ambiguity into Product Artifacts
The role requires turning vague workflow pain points into user stories and requirements. Exercise: Take a complex, unstructured business problem and draft a PRD (Product Requirements Document) that includes clear success metrics and specific user stories for an Agile engineering team.
Applied AI Pattern Identification
Fluency in generative AI, retrieval, and decision-support is required. Exercise: Review three enterprise AI use cases and categorize them by their underlying pattern (e.g., RAG for document retrieval vs. agentic workflows for automation) and justify the choice of pattern for each.
KPI Definition for AI Adoption
You must monitor adoption, cycle-time reduction, and user satisfaction. Exercise: Develop a dashboard plan for an AI-enabled product that tracks both technical performance (e.g., model accuracy) and business impact (e.g., hours saved per tender).

Your preparation checklist

Map AEC workflows to AI opportunities
Research Surbana Jurong's core business areas like construction management, tendering, and design. Prepare a brief analysis identifying where document intelligence or generative AI could specifically reduce cycle times or improve quality in these domains.
Prepare an end-to-end product case study
Select a past project where you transformed an ambiguous workflow into a digital product. Be ready to explain your process for defining user stories, managing cross-functional stakeholders, and measuring post-launch success metrics.
Develop an AI governance and evaluation framework
Draft a concise approach for how you would implement model explainability and safety guardrails for an enterprise AI tool. Focus on how you balance the need for experimentation with the requirement for reliable, production-ready output.
Formulate strategic questions for the team
Prepare questions regarding the current maturity of their AI-Core team, how they prioritize use cases across diverse business units, and the specific challenges they face in integrating AI into existing legacy construction or design workflows.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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