We are seeking an AI-Ready Knowledge Architect to play a critical role in designing and maintaining the enterprise information architecture essential for cataloging Client data for self-service understanding and enabling AI-ready 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.
Essential Job Functions:
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 AI-ready, supporting use cases ranging from traditional analytics and BI to applied machine learning and LLM-based experiences (e.g., search, retrieval-augmented 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.
Confirm and document prioritized metadata elements for key business processes, analytical use cases, and AI-enabled 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.
Required Experience:
7+ 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.
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.
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
OWL / RDF / SKOS
Protg
TopBraid
Stardog Studio
Enterprise Data & Knowledge Catalogs
Alation
Collibra
Microsoft Purview
DataHub
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)
Metadata-driven prompt context
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