Title
: Technical Product Manager – AI Platform \& Applications
Location:
Onsite \| D3, Ho Chi Minh City
Employment Type:
Full-time
Role Summary
Own the delivery of AI-powered product capabilities from concept to production.
Drive clarity in scope, requirements, and engineering execution. Operate at the
intersection of product strategy, data science, and software engineering to turn
models into reliable features that solve real problems—on time and on budget.
Core Responsibilities
Lead product discovery and translate business goals into structured, testable
technical requirements for AI features, models, and systems.
Partner with engineering, data science, and design to define architecture decisions,
data pipelines, model integration, and performance benchmarks.
Manage product lifecycle: roadmap creation, sprint planning, prioritization, release
planning, and post-launch iteration.
Define success metrics tied to usage, accuracy, latency, cost, and retention—not
vanity metrics.
Write clear PRDs, user stories, and acceptance criteria grounded in engineering
realities rather than wishful thinking.
Balance experimentation with discipline: push rapid model prototyping while
enforcing production-grade standards.
Translate complex machine learning concepts into business terms for executives
and stakeholders.
Maintain tight control over scope creep, delivery risk, and technical debt.
Evaluate vendor technologies, LLM models, frameworks, and emerging
architectures.
Ensure responsible AI use, including privacy, data protection, bias awareness, and
compliance.
Qualifications
5+ years product management experience in technical software; 2+ years shipping
AI/ML features.
Hands-on familiarity with LLMs, embeddings, vector databases, and model lifecycle
fundamentals.
Strong understanding of API design, cloud architecture, and data engineering
concepts.
Technical literacy: able to review system designs, push back on complexity, and
negotiate trade-offs.• Proven delivery track record: real shipped products, not theoretical research.
Excellent communication—precise, direct, and grounded in data.
Formal training in computer science, engineering, applied math, or similar
preferred.
What Success Looks Like
Models deployed to production that materially improve user outcomes.
Faster development cycles and fewer costly reworks.
Clear alignment between business goals and technical decisions.
Reduced ambiguity, fewer surprises, and controlled execution.