AI Product Owner (Education & Research)

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ABOUT THE POSITION
The AI Product Owner (Education & Research) turns initiatives set by the AI Product Manager into AI features that work reliably and are used by learners, instructors and researchers at VinUniversity. The role is responsible for an assigned product area, from clarifying use cases and maintaining a prioritized backlog to shaping the user experience and coordinating delivery with AI engineers and researchers. It evaluates released features through product analytics and AI evaluation, and manages pilot operations until the product is ready for operational handover. This is a hands-on role in a small team that uses AI tools throughout product work: the Product Owner builds prototypes, analyzes data directly and defines AI quality requirements. The Product Owner defines user requirements and acceptance criteria; engineers own the technical solution.
KEY RESPONSIBILITIES
1. Use Case Discovery & Requirements
Use Case Clarification
• Work with users and stakeholders in the assigned area - learners, instructors, coaches and research staff - to understand current practice and turn high-level use cases into clear goals, scope and success criteria before development starts.
• Benchmark leading learning and research products that address the same problems, and apply those insights to the definition of each feature.
Requirements & Backlog
• Write user flows, user stories and acceptance criteria that engineers can build from directly, specifying what the AI should do, what it must not do, and the quality level required.
• Own a single prioritized backlog for the area; resolve open questions before work starts and accept completed stories against their acceptance criteria.
2. UX, Prototyping & AI Tools
UX & Rapid Prototyping
• Design the user experience of each feature, including the conversational experience, through user flows and simple wireframes within the University's design system.
• Build prototypes with AI and no-code tools and test them with real users before committing engineering resources; refine the prototypes based on test results.
Using AI in Product Work
• Use AI tools as the standard toolkit to synthesize research, write specifications, support design, generate test data and prepare reports.
• Stay current with AI tools, proactively test new approaches in product work, and share effective practices with the team.
3. Delivery & Project Management
Planning & Timeline
• Plan delivery for the area against the roadmap milestones: break initiatives into releases, manage dependencies and risks, and report potential delays early.
• Coordinate shared resources such as quality assurance (QA) staff, contracted engineers and subject-matter experts.
Release Management & Approvals
• Coordinate regular planning, reviews and demos with the development team, and define the scope of each release.
• Prepare the product documentation required for Vingroup's approval checkpoints, and track findings from security reviews and user testing until they are resolved.
4. Product Analytics & AI Evaluation
Product Analytics & Experiments
• Define the metrics and data each feature must capture from the start; build and analyze dashboards of usage, engagement and outcomes.
• Design and run feature-level experiments and pilots with real users; use the evidence to decide whether to retain, revise or discontinue each feature.
AI Evaluation & User Acceptance
• Assess AI quality and readiness for release: agree evaluation criteria and benchmarks with the AI Product Manager, coordinate subject-matter experts' contributions to data labeling and evaluation, and assess accuracy, groundedness (support from provided sources), safety and cost.
• Plan and run user acceptance testing for each release, with scenarios per user group and clear pass/fail conditions.
5. Product Operations & Continuous Improvement
Pilot Operations
• During build and pilot, run day-to-day operations directly with the engineers: onboard users, provide initial user support, and assess and prioritize reported issues.
• Keep the product usable while it is still changing; track usage, AI output quality and user satisfaction.
User Feedback & Handover
• Review user and operational feedback weekly, turning usability issues, unusual use cases and unmet needs into prioritized backlog items.
• Once the product is stable, hand over day-to-day operations to the designated team with documentation, clear support responsibilities and quality monitoring in place. Remain accountable for product usage and AI output quality, and prioritize further improvements.
Mentoring & Training
• Mentor and train fresh graduates and junior team members in product discovery, requirements, prototyping and AI-assisted workflows through hands-on guidance, work reviews and regular feedback.