Semantic AI​/ML Engineer

We are seeking a Senior Semantic Engineer to design and implement semantic data frameworks that provide a shared structure for enterprise data. In this role you will focus on building and maintaining ontologies and knowledge graphs, enforcing semantic validation rules for data quality, and collaborating with AI teams to integrate these semantic structures into intelligent applications. The position is industry-agnostic, emphasizing strong semantic web expertise and the ability to apply it in any enterprise context.

• Ontology Design & Maintenance:
  Design, develop, and maintain ontologies (using OWL/RDF or similar) that model key enterprise data domains and relationships, ensuring a consistent and shared data vocabulary across the organization.
• Knowledge Graph Development:
  Build and manage enterprise knowledge graphs based on the defined ontologies, linking diverse data sources into a unified graph data model.
• Semantic Querying (SPARQL):
  Create and optimize SPARQL queries to enable efficient retrieval, integration, and analysis of data from the knowledge graph.
• Validation Rules & Data Quality:
  Implement semantic validation rules and consistency checks (e.g., using SHACL or OWL constraints) to ensure data integrity and quality within the ontology and knowledge graph.
• Integration with Enterprise Systems:
  Work closely with software engineers, data architects, and IT teams to integrate the ontology and knowledge graph into the organization's existing data infrastructure and workflows.
• Collaboration & Cross-Functional Support:
  Collaborate with cross-functional teams and stakeholders, partner with AI/ML teams, and communicate semantic concepts to non-technical stakeholders.
• Integration with AI Agents:
  Work with AI agents and large language model (LLM) teams to leverage the ontology and knowledge graph for intelligent applications.
• Standards & Best Practices:
  Stay current with emerging semantic web standards, tools, and best practices, continuously improve the semantic architecture, and contribute to internal guidelines.

• Ontology Design & Maintenance:
  Design, develop, and maintain ontologies (using OWL/RDF or similar).
• Semantic Web Proficiency:
  Strong knowledge of semantic web technologies and standards – hands‑on proficiency with OWL and RDF for ontology modelling, as well as SPARQL for querying graph data.
• Knowledge Graph
  
  Experience:
  
  Practical experience building or maintaining knowledge graphs or linked data systems in an enterprise setting.
• Data Modelling & Integration
  
  Skills:
  
  A solid understanding of data modelling principles, data architecture, and integrating heterogeneous data sources.
• Programming
  
  Skills:
  
  Proficiency in at least one programming or scripting language (such as Python, Java, or similar).

• Metadata Standards:
  Familiarity with metadata standards and vocabularies such as Dublin Core, schema.org, or other industry‑specific ontologies/taxonomies.
• AI and LLM Integration:
  Experience working on projects that involve AI agents or large language models, where ontologies or knowledge graphs were used to improve AI performance.
• Enterprise System Integration:
  Proven experience integrating semantic technologies into existing enterprise systems or data platforms.
• Tools & Platforms:
  Hands‑on experience with ontology and knowledge graph tools is beneficial.

• 💰 Tax‑Deductible Costs on a contract of employment for all development roles
• 🏋🏽 MyBenefit program (sports card, well‑being program etc.)
• 🖋 Stable employment based on an employment contract
• 🏦 Special offer of banking services
• 💻 Equipment such as a laptop and monitor
• 🎓 Lux Talent platform (webinars, training, courses with certificates)