ai engineer in semantic web

No description

• 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 support RAG, 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 enterprise data products.

• 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), SKOS, 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 such as 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, LLM integrations, and agentic AI systems;
• Knowledge of metadata management, data quality, lineage, 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.

• No conditions specified