Ontology Engineer (H/F)

SLB is seeking a highly motivated Ontology Engineer to help shape the future of industrial AI. In this role, you will work at the intersection of engineering, data, knowledge representation, and artificial intelligence to create the semantic foundation that powers the next generation of SLB digital solutions. Working closely with technology experts, domain specialists, AI researchers, data scientists, and ontology architects, you will design and develop cross-domain ontologies that formalize the knowledge of SLB products, systems, processes, and business operations. This role builds upon SLB's vision of using ontologies and knowledge graphs as a trusted semantic layer to improve interoperability, accelerate digital transformation, and enhance the accuracy of AI-driven workflows The Ontology Engineer will conceptualize and model SLB equipment, systems, and their constituent parts, as well as the engineering, manufacturing, operational, and maintenance workflows that support their entire lifecycle. By creating reusable semantic models, the role will enable AI systems to reason using trusted business concepts, relationships, constraints, and engineering knowledge rather than relying solely on statistical inference. This 12-month mission could be extended for another year in another country (VIE in the US is limited to 18 months)

• Fluent in English • Masters or PhD degree in Computer Science, preferably with a dual academic cursus in physics, mechanical or electrical science • Degree in Computer Science, Engineering, Information Systems, Knowledge Management, Data Science, or related discipline. • Experience with semantic technologies, knowledge graphs, or data modeling. • Knowledge of OWL, RDF, RDFS, SKOS, SHACL, SPARQL, Neo4j, or graph technologies. • Familiarity with AI, machine learning, Retrieval-Augmented Generation (RAG), and LLM-based systems. • Strong analytical thinking and the ability to bridge technical and business domains. • Excellent communication and collaboration skills with multidisciplinary teams.