Technical Product Manager

Technical Product Manager AI Platform & Applications

Location: Onsite | District 3, Ho Chi Minh City

Employment Type: Full-time

Role Summary

Own the delivery of product features, including AI-powered product capabilities from

concept to production. Drive clarity in scope, requirements, and engineering

execution. Operate at the intersection of product strategy, data, and software

engineering to turn models into reliable, production-ready featureson time and on

budget.

Core Responsibilities

Partner with Engineering (in Vietnam) and Product Managers (in the US) to

define architecture decisions, data pipelines, model integration, and

performance benchmarks.

Translate business goals into structured, testable technical requirements for

features, models, and systems.

Manage product lifecycle stages: 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 from Business

Requirement Documents (BRD).

Balance experimentation with discipline: enable rapid model prototyping while

enforcing production-grade standards.

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 regulatory compliance.

Qualifications

5+ years of product management experience in technical software

environments. Strong understanding of API design, cloud architecture, and data engineering

concepts.

High technical literacy: able to review system designs, challenge complexity,

and negotiate trade-offs with engineering teams.

Proven delivery track record with real, shipped products (not research-only

work).

Excellent communication skillsclear, concise, and data-driven.

Formal training in Computer Science, Engineering, or a related field is

preferred.

What Success Looks Like

AI models successfully deployed to production that materially improve user

outcomes.

Faster development cycles with fewer costly reworks.

Clear alignment between business goals and technical decisions.

Reduced ambiguity, predictable delivery, and controlled execution.