We are seeking a Lead 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 define the vision, architect, and drive the enterprise-wide adoption of 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. As a technical leader, you will mentor engineers, set standards, and shape the strategic direction of our semantic and knowledge engineering practice. Responsibilities Define the strategic roadmap for enterprise semantic and knowledge engineering initiatives, aligning them with long-term business and technology goals Lead and mentor a team of engineers, fostering technical excellence, knowledge sharing, and professional growth Lead workshops and discovery sessions with senior business and technical stakeholders to elicit, define, and document business entities, relationships, attributes, hierarchies, and competency questions Architect and own enterprise ontologies, taxonomies, controlled vocabularies, and semantic models across multiple domains Establish standards for mapping source systems and business concepts into canonical semantic representations Define architectural blueprints for scalable ELT/ETL frameworks, graph-loading processes, and semantic transformations supporting structured, semi-structured, and unstructured data Drive the design and evolution of graph databases, semantic layers, and metadata repositories to directly support RAG (Retrieval-Augmented Generation), Knowledge Graph, and Agentic AI solutions Establish and chair ontology governance frameworks, business glossary stewardship, and semantic versioning policies at the enterprise level Define best practices for automated 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 Represent the organization in cross-functional forums and influence enterprise-wide technical decisions Requirements Bachelor's or Master's degree in Computer Science, Information Systems, Data Science, Engineering, or a related field 7+ years of combined professional experience in Data Engineering, Data Architecture, Knowledge Engineering, or Semantic Technologies 1+ years of proven experience leading technical teams or initiatives Deep, hands-on expertise in semantic web technologies (RDF, OWL, SPARQL), along with SKOS and SHACL, ontology development, taxonomy creation, and knowledge graph architecture at enterprise scale Proven track record of designing and delivering enterprise-scale ELT/ETL pipelines and data integration frameworks Strong experience with cloud data platforms (Databricks, Snowflake, Azure, or AWS) Advanced coding skills in Python and SQL, alongside graph querying and reasoning capabilities Strategic understanding of modern AI patterns, including RAG architectures, vector databases, and LLM integrations, as well as agentic AI systems, with the ability to guide architectural decisions Comprehensive knowledge of metadata management, data quality, and lineage, along with governance principles and semantic versioning Demonstrated leadership and mentoring skills, with a history of guiding senior engineers and shaping technical culture Exceptional verbal and written communication skills, with the ability to articulate complex semantic and data concepts clearly to executive-level, technical, and non-technical audiences English proficiency at an Upper-Intermediate level (B2) or higher Nice to have Experience representing organizations in industry forums, publishing thought leadership, or contributing to open-source semantic technology communities Advanced background in semantic tech (RDF, OWL, SPARQL), SKOS, SHACL, and Knowledge Graphs Familiarity with AWS, Neo4j, and Amazon Neptune Deep expertise in vector databases, semantic layer platforms, and RAG integrations Experience defining enterprise architecture standards and governance frameworks across multiple business units
