Overview In this role, you help build the semantic and contextual foundation for SAP's AI, shaping how enterprise data becomes actionable knowledge. You will design and scale ontologies and semantic models to unify SAP and external data, enabling accurate AI agents across core business processes. You'll translate business challenges into concrete AI use cases, develop end-to-end ML solutions, and partner with cross-functional teams to deliver production-ready capabilities at enterprise scale. This is a chance to contribute to trustable, enterprise-ready AI at a global level and shape SAP's AI future.
Responsibilities- Leverage SAP data models, metadata structures, and process semantics to build AI/semantic data solutions
- Design and maintain enterprise ontologies and semantic models for cross-system interoperability
- Work with cloud and data platforms (Databricks, SAP Datasphere, HANA Cloud, AWS/Azure/GCP) to support AI workflows
- Translate business challenges into AI use cases, designs, and measurable outcomes
- Develop and operationalize end-to-end ML/AI solutions from preprocessing to deployment and lifecycle management
- Apply advanced ML, DL, statistical modeling, data mining, and optimization to enterprise problems
- Develop AI capabilities including generative AI and LLM-based solutions using knowledge graphs and process intelligence
- Collaborate with product, engineering, business, and customer-facing teams to ensure scalable, production-ready solutions
- Hands-on experience with SAP data platform stack (SAP Datasphere, Knowledge Graph Engine, One Domain Model, Graph API, Business Accelerator Hub) and enterprise data domains
Key requirements- Master's degree or PhD in Computer Science, Applied Mathematics, Statistics, Engineering, or related quantitative fields
- 10+ years of experience in ML, DL, statistical modeling, generative AI, and LLMs with hands-on model development and evaluation
- 10+ years of experience in ML/data science/semantic data systems in industry/research
- Strong Python and SQL skills with production-grade development and ML libraries (PyTorch, TensorFlow, scikit-learn)
- Experience deploying and operating AI/ML solutions in production with lifecycle support
- Experience with big data and cloud platforms (Databricks, AWS, Azure, GCP)
- Excellent communication, collaboration, agile experience, and curiosity for new AI techniques for SAP customers
- Deep knowledge of SAP data models, metadata, and end-to-end business processes
- Hands-on experience with SAP data/AI platform stack (SAP Datasphere, SAP HANA Cloud Knowledge Graph Engine, SAP Graph API, etc.)
- Experience designing and maintaining enterprise ontologies (OWL, RDF/RDFS, SKOS, SHACL) and querying with SPARQL, Cypher, GQL
- Experience entity resolution, deduplication, identity stitching, and semantic data harmonization
- Understanding of data products and data mesh principles
- Proven ability translating abstract business challenges into concrete AI solutions and producing adoption-ready results
- Experience working with cross-functional stakeholders in agile enterprise environments
- Experience building AI capabilities using enterprise data, knowledge graphs, or process intelligence
- Excellent communication and collaboration
- Curiosity for new AI techniques
- Customer-facing experience
- Python
- SQL
- PyTorch
