Platform Product Manager, Ontology & Knowledge Graph

Vizient•Chicago, IL

3d•$77,400 - $135,400

About The Position

In this role, you will independently own defined capabilities within the Knowledge & Semantic Platform that enable AI-powered products and consistent intelligence delivery across the organization. You will partner closely with engineering and internal clients to define and evolve the operating layer beneath the platform, including ontology, knowledge graph, metadata, entity relationships, taxonomy, and semantic models. You will manage the product backlog and roadmap for assigned platform capabilities while working in an AI-first product delivery model, leveraging AI tools to accelerate discovery, requirements development, communication, validation, and delivery support.

Requirements

2 or more years of relevant experience required.
Typically possesses 3–5 years of relevant experience in product management, product ownership, enterprise platforms, knowledge management, semantic technologies, AI-enabled products, or platform enablement.
Experience partnering with engineering and data teams to translate business concepts into scalable semantic models, entity relationships, and reusable platform capabilities.
Demonstrated experience using AI tools such as Codex/ChatGPT, Claude Code/Co-Work, GitHub Copilot/Copilot Studio, and emerging agentic coding environments to improve product discovery, requirements development, documentation, analysis, and communication.
Strong analytical, communication, collaboration, and stakeholder management skills with the ability to drive adoption, reuse, quality, and measurable business value across platform capabilities.

Nice To Haves

Relevant degree preferred.
Experience working with ontology, knowledge graphs, taxonomy, semantic modeling, metadata management, graph databases, or related knowledge management technologies preferred.
Familiarity with AI-enabled applications, retrieval-augmented generation (RAG), large language models, or other AI technologies that leverage structured knowledge and semantic data preferred.
Understanding of data stewardship, metadata governance, healthcare data concepts, and semantic consistency principles preferred.

Responsibilities

Own the product backlog and near-term roadmap for assigned Knowledge & Semantic Platform capabilities, including ontology, knowledge graph, taxonomy, metadata management, semantic modeling, and related platform services.
Conduct discovery with internal clients to understand business concepts, workflow needs, data relationships, AI use cases, and opportunities to improve semantic consistency and intelligence delivery.
Define product requirements, user stories, acceptance criteria, semantic models, entity relationships, metadata standards, prompt and context artifacts, and validation approaches that enable scalable AI-driven solutions.
Partner with engineering leads, architects, AI Delivery, data engineering, security, and design teams to translate business concepts into reusable semantic platform capabilities.
Balance delivery priorities across internal client needs, engineering capacity, architecture direction, governance requirements, data stewardship practices, and emerging AI-assisted development opportunities.
Collaborate with teams using agentic coding tools, AI-assisted development environments, and AI-supported testing, documentation, and validation workflows to improve delivery quality and speed.
Support implementation planning, release readiness, documentation, validation, and adoption activities while promoting consistent semantic standards and reusable platform capabilities.
Use metrics, operational insights, client feedback, and AI-supported synthesis to refine backlog priorities and continuously improve ontology, metadata quality, semantic relationships, and platform adoption.
Create guidance, demonstrations, communications, and enablement resources that help internal teams effectively leverage semantic platform capabilities, knowledge graph assets, and AI-first development practices.
Identify opportunities to improve metadata quality, semantic consistency, data stewardship, self-service, and reusable knowledge assets that enhance AI-enabled products and retrieval-augmented generation (RAG) capabilities.