Design and implement context architectures that enable AI systems to access, interpret, and reason over enterprise knowledge.
Develop and maintain ontologies, schemas, and knowledge representations that structure domain knowledge across systems, ensuring consistency, reusability, and scalability.
Define and optimize context assembly pipelines, including retrieval strategies, ranking logic, memory handling, and prompt/context composition for LLM-based systems.
Build and manage semantic layers over structured and unstructured data, enabling effective grounding of AI agents in real-world business context.
Design and implement knowledge graphs and context graphs to model relationships between entities, actions, and outcomes across enterprise systems.
Collaborate with AI Engineers and Data teams to align embeddings, chunking strategies, and vector storage with ontology and semantic design.
Establish standards for context quality, including evaluation frameworks for relevance, coherence, completeness, and business impact.
Enable interoperability across AI systems by defining shared context interfaces, schemas, and protocols (e.g., MCP or API-based context services).
Continuously refine context systems based on agent performance, feedback loops, and operational insights.
Translate complex semantic and contextual concepts into actionable implementations for both technical and non-technical stakeholders.

Strong experience in designing semantic systems, ontologies, or knowledge graphs within complex data environments.
Hands-on experience with knowledge representation techniques, including taxonomy design, entity-relationship modeling, and graph-based structures.
Experience working with LLM-based systems, particularly in context engineering, retrieval-augmented generation (RAG), or agentic AI architectures.
Deep understanding of embeddings, vector databases, and retrieval strategies, and how they interact with structured semantic layers.
Experience designing context pipelines that integrate multiple data sources (APIs, databases, documents) into coherent inputs for AI systems.
Familiarity with frameworks and tools related to graph databases (e.g., Neo4j), semantic layers, or metadata management.
Strong understanding of trade-offs in context construction, including latency vs. completeness, precision vs. recall, and static vs. dynamic context.
Experience working in cloud environments (AWS, Azure, GCP) and integrating AI systems into production-grade architectures.
Ability to communicate complex semantic and AI concepts clearly across technical and business stakeholders.

Staff AI Engineer, Data Ontologist

Key skills