The Position
We are seeking an experienced AI Semantic Architect to design, build, and operate the semantic foundation that powers enterprise-scale AI, intelligent agents, and knowledge-driven applications.
This role is focused on the development of the Enterprise Semantic Layer, providing a unified and governed representation of enterprise knowledge across data domains, systems, and applications. Leveraging knowledge graphs, ontologies, semantic models, metadata systems, and retrieval technologies, you will build the technical capabilities that enable AI systems to discover, retrieve, reason over, and act upon enterprise knowledge.
Unlike ontology governance or data modeling roles, this position is focused on engineering reusable platform components such as semantic services, GraphRAG architectures, MCP servers, retrieval pipelines, and agent tooling that accelerate delivery of Enterprise Brain capabilities.
Tasks and responsibilities
Semantic Layer Engineering
- Design, implement, and evolve the Enterprise Semantic Layer that abstracts enterprise knowledge and makes it consumable by AI agents, applications, and analytical platforms.
- Translate heterogeneous enterprise data sources into a unified semantic representation using knowledge graphs, ontologies, semantic models, metadata, and business context.
- Design scalable architectures enabling interoperability across data domains through shared semantics and common business vocabularies.
- Ensure enterprise-grade security, governance, scalability, observability, and maintainability of semantic platform components.
Semantic Layer & KG Development
- Design and implement knowledge graph and semantic layer architectures using RDF, OWL, SHACL, SKOS, semantic models, and graph technologies.
- Develop retrieval solutions leveraging knowledge graphs, ontologies, embeddings, metadata, and semantic search capabilities.
- Support integration of enterprise knowledge graphs with platforms such as Snowflake, Databricks, Neo4j, vector databases, search engines, and AI platforms.
Agent & MCP Engineering
- Design and implement MCP servers, semantic tools, and reusable agent-facing services.
- Develop typed tool definitions and YAML/JSON-based tool specifications for enterprise agents.
- Build reusable frameworks and accelerators for Agentic AI solutions.
- Define testing, evaluation, monitoring, and governance approaches for semantic AI and agentic systems.
Required
- 5+ years of experience in software engineering, backend engineering, data platforms, or AI platform development.
- Strong proficiency in Python and modern API development frameworks.
- Hands-on experience with semantic web technologies including RDF, OWL, SPARQL, and SHACL.
- Experience implementing and operating knowledge graph or semantic layer technologies.
- Strong understanding of software engineering best practices, Git workflows, CI/CD, testing, and cloud-native architectures.
- Ability to communicate effectively with data engineers, architects, AI engineers, and business stakeholders
Desirable
- Experience with metaphactory, Stardog.
- Experience with Snowflake Semantic Models and Open Semantic Interoperability (OSI) compliant YAML specifications.
- Experience implementing MCP servers and agent platforms.
- Experience working in regulated environments such as pharmaceutical, healthcare, or life sciences industries.