About you

We're seeking an experienced Language Knowledge Engineer to architect the semantic foundations of our federated data platform. You'll bridge the gap between enterprise data structures and modern AI systems, designing knowledge frameworks that enable seamless interaction between organizational knowledge, large language models, and AI agents. Your expertise will create the cognitive architecture that allows our platform to understand, reason about, and intelligently interact with enterprise data ecosystems.

What you bring to the table

• 5+ years of experience developing ontologies and semantic models that serve both human users and AI systems

• Deep expertise in semantic web technologies including RDF, OWL, SPARQL, and knowledge graph implementations

• Strong understanding of LLM semantic representations, embeddings, and reasoning capabilities

• Experience designing and implementing knowledge representation frameworks for enterprise systems

• Proficiency in at least one programming language commonly used for semantic modeling (Python, Java, etc.)

• Track record of translating complex business domains into structured ontologies

• Experience with ontology evaluation, validation methodologies, and quality assurance

• Strong communication skills to collaborate with both technical and business stakeholders

• Bachelor's degree in Computer Science, Linguistics, Information Science, or related field (Master's preferred)

Highly Desired Skills

• Experience integrating knowledge graphs with large language models or AI agents

• Background in enterprise data management, governance, or federated data architectures

• Knowledge of data mesh principles and domain-oriented data ownership

• Expertise in implementing semantic interoperability between disparate systems

• Familiarity with vector databases and similarity-based retrieval systems

• Experience with natural language understanding and semantic parsing

• Understanding of enterprise taxonomy management and metadata governance

• Exposure to neuro-symbolic AI approaches combining knowledge graphs with deep learning

• Previous work with federated query systems across distributed knowledge bases

You will be responsible for

• Design comprehensive knowledge engineering frameworks that enable LLMs and AI agents to interact effectively with enterprise data

• Develop ontologies and knowledge graphs that capture both business semantics and technical data structures

• Create alignment and mapping mechanisms to resolve semantic conflicts between different organizational domains

• Implement validation methodologies to ensure ontology effectiveness across human users, LLMs, and AI agents

• Collaborate with data platform engineers to integrate semantic layers into our federated data architecture

• Design knowledge retrieval patterns optimized for context-aware LLM interactions

• Develop metrics and evaluation frameworks to measure semantic coherence and AI reasoning quality

• Guide the evolution of our semantic architecture to accommodate new AI capabilities and enterprise data needs

• Partner with customer implementation teams to adapt our semantic framework to diverse industry contexts

• Stay current with emerging research in knowledge representation, LLMs, and AI reasoning systems

What we offer

Join us in shaping the future of distributed data architectures in a remote-first environment. Enjoy competitive compensation and the chance to work with cutting-edge technology.

How to Apply

Please submit your resume along with examples of ontologies or knowledge graphs you've designed, particularly those that facilitated AI system interactions. We're interested in understanding your approach to balancing formal semantic rigor with the practical needs of enterprise systems and modern AI capabilities.