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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI-Ready Knowledge Architect - **Company:** KeyCorp Insurance Agency USA Inc. - **Location:** Pittsburgh, PA, United States (Remote available) - **Experience:** Expert - **Salary:** $96,000.0 - $181,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Microsoft Azure, Data Governance, Decision Support Systems, Interoperability, Machine Learning, Metadata, Meta-Data Management, Metadata Repositories, Neo4j, Reference Data, Search Technologies, Enterprise Data Management, Large Language Models, Usage Tracking, Knowledge Representation, Question Answering, AI Platforms, Data Lineage, Cosmos DB, Data Management, Domain Model - **Published:** June 6, 2026 - **Apply:** https://diversityjobs.com/career/17172039/Ai-Ready-Knowledge-Architect-Pennsylvania-Pittsburgh ## About the Role * 10+ years of experience working with data, metadata, and reference data frameworks, including experience in metadata management and/or data quality monitoring * Experience leading the development of enterprise business glossaries, domain models, and ontologies to enable semantic consistency, shared understanding, and AI ready data usage. * Demonstrated experience with data management concepts including data governance, data quality, master data management, data lineage, and metadata management. * Proven ability to establish and operationalize metadata governance functions, including policies, standards, roles, and controls. * Demonstrated verbal and written communication skills, with strong data, metadata, and governance storytelling that drives adoption and influences stakeholders. * Hands on experience implementing and scaling an Enterprise Data Catalog or metadata repository (Alation or equivalent), including curation workflows and adoption strategies. * Understanding of how semantic models, metadata, and knowledge representation enable applied AI and LLM use cases, such as search, question answering, and decision support. * Strong business acumen in relating data to business process drivers and performance management, with a value delivery mindset. * Collaborative, team focused delivery experience that drives outcomes across enterprise data, analytics, and technology organizations. * Strategic thinker with the ability to translate enterprise objectives into actionable plans and measurable outcomes. * Excellent knowledge of data and metadata management principles, business analysis, and process engineering. TECHNOLOGIES: Knowledge Graphs Neo4j Stardog Amazon Neptune / Azure Cosmos DB (Graph) Ontology & Semantic Modeling, Knowledge Modeling Techniques Ontologies & domain models Business vocabularies & taxonomies Semantic normalization Entity & relationship modeling AI Context Delivery (Grounding Layer) Vector databases (Pinecone, Weaviate, Azure AI Search) Graph + vector retrieval (hybrid RAG) Metadatadriven prompt context ## Description The AI-Ready Knowledge Architect plays a critical role in designing and maintaining the enterprise information architecture essential for cataloging KeyBank's data for selfservice understanding and enabling AIready data and knowledge usage. This role defines and enforces standards for data modeling, taxonomy, semantic structures, and knowledge representation to ensure consistency, interoperability, and clarity across the organization. The AI-Ready Knowledge Architect partners closely with business and technology teams to develop and maintain the enterprise data domain model and ontologies that support governance frameworks, trusted analytics, and downstream consumption across business intelligence (BI), applied AI/ML, and Large Language Model (LLM) use cases. Success in this role requires the ability to translate complex theoretical concepts into scalable, governed information structures that drive adoption of the data catalog, support emerging AI capabilities, and deliver measurable value to colleagues., * Lead the development and maintenance of the enterprise data domain model, taxonomy, and ontologies to ensure shared understanding, semantic consistency, and discoverability of data and knowledge assets. * Design and evolve information and semantic models that make enterprise data AIready, supporting use cases ranging from traditional analytics and BI to applied machine learning and LLMbased experiences (e.g., search, retrievalaugmented generation, and copilots). * Operationalize data models, taxonomies, and semantic structures through the Enterprise Data Catalog (Alation). * Define and enforce standards for data modeling, taxonomy, nomenclature, and semantic structures to ensure consistency and interoperability across business domains and downstream consumption patterns. * Provide authoritative guidance on semantic conflicts-resolve definition discrepancies, harmonize terms, and mediate crossdomain dependencies to establish trusted, reusable business meaning. * Contribute to the enterprise data product framework by defining domain boundaries, shared dimensions, and semantic contracts that enable crossdomain interoperability and AI consumption. * Confirm and document prioritized metadata elements for key business processes, analytical use cases, and AIenabled workflows, ensuring alignment with governance standards and risk expectations. * Identify simplification opportunities-reduce redundancy, converge overlapping datasets, and promote canonical sources to improve trust, efficiency, and reusability across analytics and AI platforms. * Partner with analytics, data science, and AI engineering teams to ensure information architecture, metadata, and semantic context are sufficient to support explainable, governed, and trustworthy AI outcomes. * Serve as a thought partner, provide insights from modeling, catalog adoption, and AI enablement to shape governance strategy and roadmaps. ## Related Videos - [New AI-Centric SDLC: Rethinking Software Development with Knowledge Graphs](https://www.wearedevelopers.com/videos/1417-new-ai-centric-sdlc-rethinking-software-development-with-knowledge-graphs) - [Putting the Graph In GraphQL With The Neo4j GraphQL Library](https://www.wearedevelopers.com/videos/257-putting-the-graph-in-graphql-with-the-neo4j-graphql-library) - [A Data Mesh needs Open Metadata](https://www.wearedevelopers.com/videos/505-a-data-mesh-needs-open-metadata) - [Developer Tools for Microsoft Azure](https://www.wearedevelopers.com/videos/450-developer-tools-for-microsoft-azure) - [Building an AI-Ready Content Lake: Scaling RAG and Document AI Beyond Demos](https://www.wearedevelopers.com/videos/1977-building-an-ai-ready-content-lake-scaling-rag-and-document-ai-beyond-demos) - [Knowledge graph based chatbot](https://www.wearedevelopers.com/videos/754-knowledge-graph-based-chatbot) ## Related Articles - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Everything a Developer Needs to Know About MCP with Neo4j](https://www.wearedevelopers.com/magazine/604-everything-a-developer-needs-to-know-about-mcp-with-neo4j) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)