New opportunity

Product Manager – AI Agent & Chatbot Intelligence, Regional Operations (2026 Graduate)

Shopee · Singapore

About this role

Shopee is seeking an entry-level Product Manager to develop an AI assistant for customer support that serves customers and human agents and improves through agent feedback and real-world interactions. The role is based in Singapore.

Responsibilities

Lead chatbot and agent–AI interaction features from concept through rollout and iteration. Design feedback loops and annotation workflows that let human agents supervise, correct and train AI models in real time, including through reinforcement learning from human feedback.
Partner with engineering, data science, operations and design to deliver products combining LLMs and agentic logic at operational scale. Align business, operations and technology teams around solutions that balance automation with human judgment.
Define user journeys, product metrics and evaluation frameworks to improve model quality, customer-support deflection rates and Customer Satisfaction Score (CSAT). Build chatbot lifecycle tools, including training and annotation platforms, intent classification, knowledge integration and automated evaluation pipelines.
Track developments in generative AI, including prompting, fine-tuning and multi-agent orchestration, and translate them into product opportunities.

Requirements

Bachelor’s degree in Computer Science, Engineering or a related technical field.
Familiarity with agentic system design, prompt orchestration and human-in-the-loop approaches; ability to investigate product metrics, model-performance dashboards and user-behaviour insights.
Equivalent experience building and scaling AI products is a plus. Experience shipping products powered by LLMs, such as GPT or Claude, or by conversational AI frameworks is also 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 managementLarge Language Models (LLMs)Conversational AIAgentic systemsHuman-in-the-loop systemsReinforcement learning from human feedbackAnnotation workflowsPrompt orchestrationIntent classificationKnowledge integrationModel evaluationProduct metricsData analysisCross-functional collaboration

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

Own chatbot features through iteration
The role leads chatbot and agent–AI interaction features from concept through rollout and ongoing improvement. Preparation suggestion: be ready to explain how a product decision would change after observing real customer and agent interactions.
Make agent feedback usable for improvement
The posting calls for real-time agent supervision and correction, supported by feedback loops and annotation workflows. Preparation suggestion: map how a correction could become a reviewed, usable signal for improving the assistant.
Measure support outcomes alongside model quality
The PM defines user journeys, metrics and evaluation frameworks covering model quality, deflection rate and CSAT impact. Preparation suggestion: consider what each measure reveals—and what it could miss.
Build scalable chatbot lifecycle tools
The stated scope includes training and annotation platforms, intent classification, knowledge integration and automated evaluation pipelines. Preparation suggestion: think through which tooling would make repeated updates and evaluation manageable at operational scale.
Align teams on practical AI choices
The PM works with engineering, data science, operations and design, while aligning business, operations and technology teams. The role also translates developments in prompting, fine-tuning and multi-agent orchestration into product opportunities. Preparation suggestion: frame technology choices in terms of support needs and operational trade-offs.

Likely challenges to think through

Automation versus human judgment
Likely challenge, not a confirmed Shopee problem: increasing deflection could conflict with appropriate agent involvement or customer satisfaction. Prepare by defining when an assistant should answer, seek agent review or hand off, and how to assess those decisions.
Learning from corrections without burdening agents
Likely challenge, not a confirmed Shopee problem: richer annotations may improve feedback quality but take time away from support work. Prepare by sketching a lightweight correction flow and criteria for deciding which feedback merits review.
Interpreting mixed evaluation signals
Likely challenge, not a confirmed Shopee problem: a model-quality measure, deflection rate and CSAT may move in different directions. Prepare to explain how journey-level evidence and agent observations could help investigate a metric change before rollout or iteration decisions.

Practice your answers

Technical practice

How would you take a customer-support chatbot feature from concept through rollout and iteration?

What to coverThe role covers the full lifecycle of chatbot and agent–AI features. Preparation suggestion: explain how you would define a user journey, choose an initial scope, measure results after rollout and decide what to improve.

How would you design a workflow for human agents to correct an AI assistant?

What to coverThe posting calls for real-time supervision, feedback loops and annotation workflows. Preparation suggestion: discuss how agents would submit corrections, how those corrections would be reviewed and how useful feedback would reach the product or model team.

What role could human feedback play in improving model behaviour?

What to coverReinforcement learning from human feedback is given as an example of the role’s work. Preparation suggestion: distinguish collecting and evaluating feedback from using it to train a model, and address the risk of inconsistent annotations.

How would you decide when a support assistant should act autonomously or involve a human agent?

What to coverThe role combines LLMs with agentic logic while balancing automation and human judgment. Preparation suggestion: describe decision boundaries, escalation signals and the trade-off between resolving issues efficiently and avoiding poor customer outcomes.

How would you define metrics for model quality, deflection and CSAT?

What to coverThe posting explicitly names all three as areas to track and improve. Preparation suggestion: explain what each metric captures, how you would interpret them together and why higher deflection alone might not mean a better customer experience.

How would you evaluate a chatbot before and after a change?

What to coverThe role includes evaluation frameworks and automated evaluation pipelines. Preparation suggestion: describe representative support interactions, quality criteria and checks for regressions, then explain how post-rollout results would inform iteration.

How would intent classification fit into the chatbot lifecycle?

What to coverIntent classification is one of the lifecycle capabilities named in the posting. Preparation suggestion: explain how it could help route or interpret requests, and what you would inspect when similar intents are confused.

How would you make support knowledge useful to an AI assistant?

What to coverKnowledge integration is listed among the tools this role would build. Preparation suggestion: discuss how the assistant would find relevant information, how you would handle missing or outdated knowledge and when uncertainty should trigger human review.

When might prompting, fine-tuning or multi-agent orchestration be worth exploring?

What to coverThe role asks the Product Manager to track these approaches and translate developments into product opportunities. Preparation suggestion: compare their potential value against complexity, evaluation effort and the specific support problem being addressed.

A model-performance dashboard worsens after rollout; how would you investigate?

What to coverThe requirements call for comfort investigating dashboards, product metrics and user behaviour insights. Preparation suggestion: identify which interactions changed, compare quality and customer outcomes, examine agent feedback and propose a focused next test.

Behavioural practice

Tell me about a time you aligned people with different priorities on a product or project decision.

What to coverThe role involves collaboration across engineering, data science, operations, design and business teams. Preparation suggestion: use a real example, including an academic project if relevant, and explain the situation, your actions and the result.

Describe a time you changed your approach after receiving feedback from the people using your work.

What to coverThe assistant described in the posting is intended to improve through agent feedback and real-world interactions. Preparation suggestion: give a real situation, the feedback you received, what you changed and the result.

Tell me about a time data challenged your initial assumption.

What to coverThe posting emphasizes product metrics, model-performance dashboards and user behaviour insights. Preparation suggestion: describe the situation, how you investigated the evidence, the action you took and what happened.

Describe a time you had to decide what to automate and what needed human judgment.

What to coverBalancing automation with human judgment is an explicit responsibility of the role. Preparation suggestion: choose a real project or process example and explain the situation, your decision, your actions and the outcome.

Tell me about a time you assessed a new technical idea for a practical use.

What to coverThe role calls for translating developments in generative AI into product opportunities. Preparation suggestion: use a real example, whether or not it involved AI, and explain the situation, how you tested its relevance, what you recommended and the result.

Sharpen your knowledge

Topics to prepare

Agentic systems and prompt orchestration
Review how an LLM-based support assistant could route a request, use knowledge, and hand off to a human agent. Sketch where prompts and agentic logic affect each step. The posting explicitly asks for familiarity with both concepts.
Human-in-the-loop learning
Trace a hypothetical incorrect answer from agent correction through annotation, review, and possible model improvement. Distinguish an immediate correction from later training or reinforcement learning from human feedback. The role calls for agent supervision and feedback loops.
Support-product evaluation
Define what model quality, deflection rate, and Customer Satisfaction Score (CSAT) would each reveal—and what each could miss—in a sample support journey. These are the outcomes the posting says the PM will track and improve.
Chatbot lifecycle components
Draw a conceptual flow connecting intent classification, knowledge integration, training and annotation, and automated evaluation. Explain where an error might enter and how it could be detected. The posting names these as lifecycle tools to build.
Generative-AI product trade-offs
Compare prompting, fine-tuning, and multi-agent orchestration for one hypothetical support problem; state what evidence you would seek before choosing an approach. The posting asks the PM to follow these developments and translate them into product opportunities.

Skills to revise

Map a customer-and-agent journey
Practice drafting a one-page journey from customer question to answer, escalation, agent correction, and follow-up iteration. Mark the decision points requiring human judgment. This exercises the posted responsibility for chatbot and agent–AI features from concept through rollout.
Specify an annotation workflow
Practice writing a short workflow for agents to flag, correct, and label a bad response, including what information a reviewer would need. Treat the workflow as a practice design, not an existing Shopee process; the posting calls for feedback and annotation tools.
Investigate a metrics dashboard
Using a hypothetical dashboard, compare changes in deflection, CSAT, and model-quality measures; identify a segment to investigate and propose a follow-up question. The requirements explicitly call for comfort with product metrics, performance dashboards, and user-behaviour insights.
Draft evaluation cases
Write a small set of hypothetical support queries covering different intents, missing knowledge, and cases needing handoff. Specify the expected outcome and how you would record a failure. This practices the posting’s evaluation-framework and auto-evaluation responsibilities.
Frame a cross-functional rollout decision
Practice a brief proposal stating the user problem, an initial scope, success measures, operational risks, and questions for engineering, data science, operations, and design. Those partners and the need to balance automation with human judgment are explicit in the posting.

Your preparation checklist

Gather evidence of technical foundations
Prepare a concise account of relevant Computer Science, Engineering, or related technical study. If applicable, identify coursework or projects showing familiarity with agentic systems, prompt orchestration, and human-in-the-loop design; do not present preferred shipping experience as required.
Build a project walkthrough
Choose an actual AI or conversational-product project, if you have one, and outline the user problem, your decisions, collaborators, results, and what you would change. If you lack one, use a clearly labeled hypothetical support-assistant case instead.
Sketch a support journey and feedback loop
As a practice exercise, map a customer request from intent recognition through AI response, possible human-agent intervention, agent correction, annotation, and subsequent improvement. Mark where human judgment should take precedence.
Draft a measurement and evaluation plan
Propose how to examine model quality, support deflection, and CSAT alongside agent and customer behaviour. Prepare a sample dashboard and explain how you would detect when apparent automation gains harm the support experience.
Outline a small feature rollout
Write a one-page proposal for a chatbot or agent–AI interaction feature: problem, user journey, initial scope, dependencies across engineering, data science, operations, and design, rollout checks, and an iteration decision based on feedback.
Compare implementation options
Prepare a brief trade-off table for prompting, fine-tuning, and agentic orchestration, plus where knowledge integration, annotation tools, and automated evaluation would fit. Treat the architecture as a discussion exercise, not Shopee’s stated design.
Prepare questions for the interviewers
Ask how customer and human-agent needs are prioritized, how agent corrections enter evaluation or training, and how the team balances model quality, deflection, CSAT, and operational scalability.

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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