Information Architect Knowledge Graph & AI Enablement

Role Overview

We are seeking an experienced Information Architect to lead the design and implementation of enterprise knowledge graphs and semantic data structures to power AI-driven solutions. This role requires deep expertise in metadata management, schema design, ontology creation, and integration of structured and unstructured data from multiple enterprise systems. The Information Architect will partner closely with AI/ML, engineering, and business stakeholders to deliver scalable information models that enable advanced reasoning, retrieval-augmented generation (RAG), and generative AI applications.

Key Responsibilities

1. Knowledge Graph & Ontology Design
1. Study enterprise metadata, business glossaries, and data dictionaries across multiple connected applications.
2. Develop ontologies, taxonomies, and semantic schemas that consolidate disparate data models into a unified, reusable structure.
3. Build and maintain scalable knowledge graphs for AI/ML consumption, ensuring semantic consistency and traceability.
3. Schema & Metadata Integration
1. Map and normalize data across heterogeneous systems (databases, APIs, cloud apps, documents).
2. Define metadata standards, relationships, and entity mappings for cross-domain knowledge representation.
3. Ensure data models align with compliance, governance, and security policies.
5. AI Enablement
1. Architect data flows to support retrieval-augmented generation (RAG), semantic search, and LLM-powered applications.
2. Partner with AI engineers to integrate knowledge graph structures with OpenAI, LangGraph, Azure Cognitive Search, and other AI frameworks.
3. Optimize data indexing, embeddings, and vector search pipelines for AI consumption.
7. Architecture & Governance
1. Develop reference architectures, best practices, and documentation for semantic models and knowledge graph solutions.
2. Collaborate with enterprise architects and business SMEs to align ontologies with organizational priorities.
3. Define lifecycle management processes for schemas, ontologies, and graph evolution.
9. Innovation & Research
1. Evaluate emerging tools and technologies for knowledge graph creation, semantic data management, and AI orchestration.
2. Conduct proofs of concept (POCs) to demonstrate new approaches for scalable graph-based architectures.

Required Qualifications

1. Bachelor s or Master s in Information Science, Computer Science, Data Engineering, or related field.
2. 5+ years of experience in information architecture, knowledge management, or semantic data modeling.
3. Proven track record designing and deploying knowledge graphs, ontologies, and semantic schemas in enterprise settings.
4. Strong expertise with metadata management, schema harmonization, and data integration across multiple applications.
5. Experience with Azure ecosystem: Azure Cognitive Search, Azure Data Lake, Azure Synapse, Azure AI.
6. Hands-on experience with OpenAI APIs, LangGraph (or LangChain), vector databases, and other AI knowledge graph frameworks.
7. Proficiency in graph databases (Neo4j, Stardog, Azure Cosmos DB Graph, RDF/SPARQL).
8. Familiarity with embeddings, RAG pipelines, and LLM-driven search/reasoning.

Preferred Skills

1. Experience with enterprise knowledge graph tools (e.g., TigerGraph, Stardog, Anzo, GraphQL).
2. Knowledge of data governance frameworks and regulatory compliance (GDPR, SOX, etc.).
3. Strong communication skills to bridge technical and non-technical stakeholders.
4. Prior work in large-scale AI/ML or digital transformation.