AI-Ready Knowledge Architect
61743
Target rate: $85/hr w2
Contract Length: 07/13/2026 – 12/31/2026
Location: Remote
***Candidates must work on our W2 without needing sponsorship at any time now or in the future
***We do not work with Corp to Corp in any manner including any form of referral bonus.
If after reading the description you would like to be submitted, please complete the questions below. Answer in “implied first person” and in a stand-alone sentence. These will go along with the resume to the client.
- How many years of experience are you bringing with enterprise metadata management and/or data quality monitoring (including reference data frameworks), and in what environments?
- What enterprise business glossary work have you executed (scope/size), and how did you drive adoption and semantic consistency across domains?
- What domain models and/or ontologies have you led end-to-end (tools/standards used such as OWL/RDF/SKOS), and what was the business outcome they enabled?
- What hands-on experience are you bringing with knowledge graphs/graph platforms (e.g., Neo4j, Stardog, Amazon Neptune, Azure Cosmos DB Graph), including what you modeled and how it was used?
- What experience are you bringing supporting AI/LLM use cases (e.g., search, Q&A, RAG/copilots), and how did semantic models + metadata improve the results/governance?
- What experience are you bringing operationalizing semantic structures through a data/knowledge catalog (e.g., Alation/Collibra/Purview/DataHub), including standards you enforced?
JOB DESCRIPTION:
We are seeking an AI-Ready Knowledge Architect to play a critical role in designing and maintaining the enterprise information architecture essential for cataloging the organization’s 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:
- 7-10 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.
- 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
- 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)
- Metadata-driven prompt context