Senior AI Engineer with Semantic Web Tech

We are seeking aSenior AI Engineer with Semantic Web Technologies to serve as the strategic bridge between business stakeholders and technical implementation teams, designing, building, and operationalizing enterprise semantic and data foundations. This role translates 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

• 5+ years of combined professional experience in Data Engineering, Data Architecture, Knowledge Engineering, or Semantic Technologies
• Bachelor's or Master's degree in Computer Science, Information Systems, Data Science, Engineering, or a related field
• Expertise in semantic web technologies (RDF, OWL, SPARQL), ontology development, and knowledge graph architecture
• Skills in taxonomy creation and controlled vocabulary development
• 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
• Knowledge of metadata management, data quality, lineage, and governance principles
• Exceptional verbal and written communication skills, with the ability to articulate complex semantic and data concepts clearly to diverse technical and non-technical stakeholders
• Proficiency in English at a B2+ level

Nice to have

• Familiarity with SKOS and SHACL for semantic modeling
• Skills in graph database platforms such as Neo4j and Amazon Neptune
• Understanding of semantic layer platforms and RAG integrations