AI-Ready Knowledge Architect

Location:

JOB DESCRIPTION:

The AI-Ready Knowledge Architect plays a critical role in designing and maintaining the enterprise information architecture essential for cataloging KeyBanks 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.

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 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 conflictsresolve 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 opportunitiesreduce 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.

REQUIRED EXPERIENCE:

• 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

OWL / RDF / SKOS

Protégé

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)

Metadatadriven prompt context

#LI-BS2

COMPENSATION AND BENEFITS

Please click here for a list of benefits for which this position is eligible.

Key has implemented an approach to employee workspaces which prioritizes in-office presence while providing flexible options in circumstances where roles can be performed effectively in a mobile environment.

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