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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Engineer with semantic web tech - **Company:** EPAM Systems, Inc. - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Microsoft Azure, Information Systems, Data Architecture, Information Engineering, Data Governance, Data Infrastructure, Data Integration, Extract Transform Load (ETL), Graph Database, Python (Programming Language), Knowledge Management, Meta-Data Management, Metadata Repositories, Neo4j, Cloud Services, Semantic Web, SPARQL, SQL Databases, Unstructured Data, Management of Software Versions, Retrieval-Augmented Generation, Large Language Models, Snowflake, Data Layers, Information Technology, Data Management, Virtual Agents, Software Coding, Data Pipelines, Databricks - **Published:** September 24, 2026 - **Apply:** https://www.dice.com/job-detail/e8393cb4-91e1-4e68-8148-28fbbf68ac6c ## About the Role We are seeking a Senior AI Engineer with deep expertise in semantic web technologies to act as the strategic bridge between business stakeholders (Data Governance, Enterprise Architecture, AI/Analytics teams) and technical implementation teams. In this role, you will design, build, and operationalize enterprise semantic and data foundations, translating complex business terminology, metadata requirements, and domain concepts into structured ontologies, knowledge graphs, and scalable data pipelines that power Analytics, AI, Agentic AI, Knowledge Management, and digital solutions. Responsibilities Lead workshops and discovery sessions with business and technical stakeholders to elicit, define, and document business entities, relationships, attributes, hierarchies, and competency questions Develop and maintain enterprise ontologies, taxonomies, controlled vocabularies, and semantic models Map source systems and business concepts into canonical semantic representations Design, develop, and maintain scalable ELT/ETL frameworks, graph-loading processes, and semantic transformations supporting structured, semi-structured, and unstructured data Implement and optimize graph databases, semantic layers, and metadata repositories to directly support RAG (Retrieval-Augmented Generation), Knowledge Graph, and Agentic AI solutions Establish ontology governance frameworks and manage the business glossary and semantic versioning Automate data quality validation, monitoring, lineage, and observability processes Partner with Data Architects, Solution Architects, Data Stewards, and AI Engineers to ensure semantic consistency, discoverability, and high data quality across all enterprise data products Requirements Bachelor's or Master's degree in Computer Science, Information Systems, Data Science, Engineering, or a related field 5+ years of combined professional experience in Data Engineering, Data Architecture, Knowledge Engineering, or Semantic Technologies Expertise in semantic web technologies (RDF, OWL, SPARQL), along with SKOS and SHACL, ontology development, taxonomy creation, and knowledge graph architecture Proficiency in building enterprise-scale ELT/ETL pipelines and data integration frameworks Familiarity with cloud data platforms (Databricks, Snowflake, Azure, or AWS) Advanced coding skills in Python and SQL, alongside graph querying and reasoning capabilities Understanding of modern AI patterns, including RAG architectures, vector databases, and LLM integrations, as well as agentic AI systems Knowledge of metadata management, data quality, and lineage, along with governance principles and semantic versioning Exceptional verbal and written communication skills, with the ability to articulate complex semantic and data concepts clearly to diverse technical and non-technical stakeholders English proficiency at an Upper-Intermediate level (B2) or higher Nice to have Background in semantic tech (RDF, OWL, SPARQL), SKOS, SHACL, and Knowledge Graphs Familiarity with AWS, Neo4j, and Amazon Neptune Skills in vector databases, semantic layer platforms, and RAG integrations ## Related Videos - [Putting the Graph In GraphQL With The Neo4j GraphQL Library](https://www.wearedevelopers.com/videos/257-putting-the-graph-in-graphql-with-the-neo4j-graphql-library) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [Semantic AI: Why Embeddings Might Matter More Than LLMs](https://www.wearedevelopers.com/videos/1460-semantic-ai-why-embeddings-might-matter-more-than-llms) - [Beyond Dashboards: Fixing Text-to-SQL with Semantic RAG](https://www.wearedevelopers.com/videos/2036-beyond-dashboards-fixing-text-to-sql-with-semantic-rag) - [Beyond SQL Generation: How to Teach Agents What Your Database Actually Means](https://www.wearedevelopers.com/videos/100127-beyond-sql-generation-how-to-teach-agents-what-your-database-actually-means) - [Cyber Sleuth: Finding Hidden Connections in Cyber Data](https://www.wearedevelopers.com/videos/893-cyber-sleuth-finding-hidden-connections-in-cyber-data) ## Related Articles - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)