Knowledge Graph Tech Lead (m/f/d)

We help the world run better. At SAP, we keep it simple: you bring your best to us, and we'll bring out the best in you. We're builders touching over 20 industries and 80% of global commerce, and we need your unique talents to help shape what's next. The work is challenging – but it matters. You'll find a place where you can be yourself, prioritize your wellbeing, and truly belong.

What you'll build

At SAP Business AI, we are exploring the next frontier of AI on structured business data like graphs, tables and more. Foundation Models on structured data, like SAP-RPT-1, have ushered in the GenAI era for business relevant scenarios. Now, we are pushing further. We want to enable companies across all industries to collaboratively train foundation models and build knowledge graphs. As Knowledge Graph Tech Lead (f/m/d), you will own the technical vision and roadmap for SAP’s Industry Knowledge Graph (IKG) – a unified, semantically rich representation of business entities, processes, and relationships across SAP applications and customer industries. You provide technical leadership for a team of knowledge engineers, data engineers, and AI scientists, leading through expertise, mentorship, and influence rather than direct people-management responsibility, enabling the modeling of industry data into a production-grade RDF Knowledge Graph for open-source delivery, shaping the IKG into a platform that other workstreams (Multi-Modal Industry Foundation Models and Data Platform) and SAP product teams build on, and pushing the frontier of how Knowledge Graphs and Large Language Models combine to enable the creation of new knowledge at scale. You move easily between deep technical work – ontologies, SHACL shapes, SPARQL, KG construction pipelines – and shaping the broader IKG architecture and roadmap that several teams across the initiative will execute against.

The Role

• Define and own the architectural and research roadmap for the Industry Knowledge Graph – ontology design, SHACL constraints, KG construction at scale, and the path from prototypes to production and delivery as open-source project.
• Provide technical and thought leadership to a multidisciplinary team of knowledge engineers, data engineers, and AI scientists; set the technical bar, drive hypotheses and experiments, and steer execution from concept through production (without direct people-management responsibility).
• Redefine what end-to-end KG construction means in the age of Agentic AI – from initial modeling to mapping diverse sources all the way to researching joint representations of latent and explicit knowledge.
• Shape the IKG to become part of the OP4I platform with clear interfaces that the MMIFM and SAP product teams build on, with documentation, blueprints and onboarding for downstream consumers.
• Push the frontier of Knowledge Graph and Foundation Model integration through agent grounding, agentic knowledge engineering, and the use of the IKG to train better foundation models, partnering with the MMIFM team and product stakeholders.

What you bring

• PhD or Master’s degree in Computer Science, Artificial Intelligence, Knowledge Representation, or related disciplines.
• Typically 12+ years of relevant industry and/or research experience in Knowledge Graphs, data engineering, AI, or related fields, including experience driving complex initiatives from concept to production.
• A strong vision of how KG construction should be done in the age of Agentic AI, demonstrated e.g. by recent academic publications in top tier conferences.
• Deep, hands-on expertise in the RDF Knowledge Graph stack (RDF, RDFS, OWL, SHACL, SPARQL) with a track record of taking enterprise Knowledge Graphs from concept to production at scale.
• Demonstrated experience leading the technical direction of a KG / data engineering effort – ontology design, KG construction pipelines, and integrating the graph into a platform that other teams build on.
• Hands-on experience integrating Knowledge Graphs with LLMs – e.g. graph RAG, KG embeddings, KG-grounded agents, hallucination mitigation.
• Knowledge of SAP business data and applications – data models, business objects, extraction interfaces – is a strong plus; broader familiarity with enterprise data (ERP, CRM) is welcome.
• Familiarity with the triple-store ecosystem (commercial and open-source: GraphDB, Stardog, Virtuoso, Apache Jena, Blazegraph, Neptune, etc.), SPARQL query optimization, and KG operations at scale is a plus.
• Proven technical leadership and outstanding communication skills for technical and non-technical audiences.
• You can defend an ontology decision in a deep technical review just as effectively as you can explain the value of a Knowledge Graph to business stakeholders and executives.
• Excellent written and verbal English communication skills at business level are required; additional languages are a plus.