Senior AI Product Owner
CBTW
- Vietnam
- Permanent
- Full-time
- Plan and prioritize product development and product feature backlog
- Develop detailed product feature specifications and ensure they’re clearly understood by relevant teams
- Assess value, develop cases, and prioritize stories, epics, and themes to ensure work aligns with product strategy
- Mitigate roadblocks to achieving sprint/release goals
- Lead the product-release plans and set expectations for delivery of new functionalities
- Experience: 10+ years of experience in Product Management or Product Ownership, with demonstrated ownership of complex product domains or platform services.
- Communication: Exceptional verbal and written communication skills in English, with the ability to distill complex technical concepts for non-technical executive stakeholders.
- Influence: Advanced negotiation and stakeholder management skills, specifically within large, matrixed organizations involving both billable client work and internal platform scaling.
- Frameworks: Mastery of Agile methodologies (Scrum/Kanban); experience with Scaled Agile Framework (SAFe) is highly preferred.
- Platform Mastery: Extensive experience managing "Platform-as-a-Product" (PaaP) or complex back-end service domains.
- Domain Knowledge: Experience working with enterprise platforms such as eCommerce, CRM, financial/billing systems, or similar complex business platforms.
- AI Proficiency: Hands-on experience with AI productivity and development tools (e.g., ChatGPT or Claude for requirements drafting and product analysis, GitHub Copilot or similar tools for technical collaboration, or Jira-integrated AI for backlog insights). Familiarity with modern AI platform capabilities such as LLM-powered services, semantic search or knowledge retrieval systems, and AI-driven data analysis.
- SAFe Product Owner / Product Manager (POPM) Certification.
- Technical Background: Prior experience as a Principal Software Engineer or Technical Architect.
- Technical Literacy: A strong conceptual understanding of RESTful APIs, event-driven architectures and messaging platforms (Kafka/RabbitMQ), SQL/NoSQL databases, cloud infrastructure (e.g., AWS), and observability (monitoring/logging). Familiarity with data structures, data pipelines, and modern AI system patterns such as vector embeddings, semantic search, and Retrieval-Augmented Generation (RAG), including concepts like document chunking and knowledge indexing used in LLM-powered applications.