Title: Product Manager - AI Knowledge Graph & Extraction Contract: 7 Months with good chance of conversion to permanent
Salary Range: to HKD120,000 (depending on experience)
About the Role We are looking for a hybrid Product Manager to lead the design and roadmap of our next-generation Knowledge Graph platform. You will work with a team that builds AI agents designed to ingest, process, and extract entities, relationships, and facts from vast amounts of unstructured documents. The goal of the product you manage is to transform this chaotic data into a structured, searchable knowledge base that enables advanced AI applications, such as Retrieval-Augmented Generation (RAG), semantic search, streamlining our ability to respond to changing regulations and business risk appetite, and many other downstream use-cases. Key Responsibilities - Product Strategy & Roadmap: Work with our stakeholders, the AI Product Lead, and Usability team to define the strategy and roadmap for building an automated, agentic pipeline that turns unstructured data into structured graph entities and relationships.
- Feature Development: Define features and work with our business analysts and AI team to develop clear, actionable user stories that progress these features.
- Prioritization: Collaborate with our product owner and use-case owners on the prioritization of features. Support the team by making prioritization calls on detailed story tickets to reduce time to value for the business.
- Team Orchestration: Run core team ceremonies including feature planning, stand-ups, backlog grooming, and retrospectives.
- Cross-Functional Leadership: Act as the translator between business stakeholders, AI Engineers, Data Scientists, and Development Engineers to bridge the gap between AI capabilities and user needs.
- Governance: Ensure governance requirements, like AI / Model governance, are considered and integrated into the technical roadmap. Work with the team to establish guardrails to prevent AI "hallucinations," enforce privacy, and ensure data lineage and quality within the knowledge base.
Qualifications - Experience in technical product management, specifically managing AI, ML, or data-intensive applications.
- Proven track record of running effective team meetings, ceremonies, and Agile workflows.
- Strong facilitation, communication, and conflict-resolution skills.
- Deep understanding of the software development lifecycle (SDLC) and product prioritization frameworks.
- Experience with Agile project tracking software like Jira and documentation tools like Confluence.
Preferred Skills - Technical Proficiency: Deep understanding of Large Language Models (LLMs), RAG and GraphRAG systems, NLP, and vector databases.
- KG Expertise: Experience with Knowledge Graphs (e.g., Neo4j, Falkor) or semantic technologies (RDF, OWL) is strongly preferred
- Agentic AI Knowledge: Familiarity with autonomous agent frameworks (e.g., AutoGen, LangChain, LangGraph) for document analysis is highly desirable.
Key Focus Areas for Candidate Selection - Starting Small, Scaling Big: Ability to evidence value with a single use case before scaling enterprise-wide data ingestion.
- Agentic Framework Knowledge: Familiarity with the stages of AI Agent-driven KG construction: Ontology expansion, refinement, population, and validation.
- Quality & Reliability: Focus on building trust with stakeholders and use-case owners through defining features to develop rigorous methods of evaluation and validation of the knowledge base. Also, defining features to keep the graph fresh and auditable by defining a versioning mechanism that indicates what is current and what is out-of-date as information changes over time.
