Contract Opportunity – Remote (CST/EST Preferred)
Pay Rate: $85.00/hr on W2 (no C2C)
Visa Status: Sponsorship not available

Overview

We are seeking an AI‑Ready Knowledge Architect to design, evolve, and maintain the enterprise information architecture that powers KeyBank’s data catalog, semantic consistency, and AI‑ready knowledge ecosystem. This role is responsible for defining and governing the models, taxonomies, ontologies, and semantic structures that ensure data is discoverable, interoperable, and usable across analytics, BI, AI/ML, and LLM‑driven experiences.

The ideal candidate blends deep expertise in metadata, semantic modeling, and knowledge representation with the ability to translate complex concepts into scalable, governed structures that drive adoption and deliver measurable business value.

Key Responsibilities

• Lead the creation and ongoing refinement of enterprise data domain models, taxonomies, and ontologies to ensure shared understanding and semantic consistency.

• Design and evolve semantic and information models that make enterprise data AI‑ready for analytics, BI, ML, and LLM use cases (e.g., search, RAG, copilots).

• Operationalize semantic structures and metadata standards within the Enterprise Data Catalog (Alation).

• Define and enforce standards for data modeling, taxonomy, nomenclature, and semantic structures across business domains.

• Document prioritized metadata elements for key business processes, analytical use cases, and AI‑enabled workflows, ensuring alignment with governance and risk expectations.

• Identify opportunities to simplify and rationalize data assets—reducing redundancy, converging overlapping datasets, and promoting canonical sources.

• Partner with analytics, data science, and AI engineering teams to ensure metadata, semantic context, and information architecture support explainable, governed, and trustworthy AI outcomes.

Required Experience

• 7–10 years working with data, metadata, and reference data frameworks, including metadata management and/or data quality monitoring.

• Proven experience developing enterprise business glossaries, domain models, and ontologies to support semantic consistency and AI‑ready data usage.

• Understanding of how semantic models, metadata, and knowledge representation enable applied AI and LLM use cases (search, Q&A, decision support).

• Strong business acumen with the ability to connect data structures to business processes and value delivery.

• Demonstrated success collaborating across enterprise data, analytics, and technology teams.

• Deep knowledge of data and metadata management principles, business analysis, and process engineering.

Relevant 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)

• Hybrid retrieval (graph + vector)

• Metadata‑driven prompt context

Anna Karoutsos
Talent Sourcing Partner