Senior Graph Implementation Lead, Expert Services; m​/f​/d

The Graph Expert Services team is responsible for designing the backbone of AI readiness for our customers. Our platform combines visual, low‑code modeling with graph‑native context so enterprises keep relationships, lineage, and meaning intact—critical for trustworthy AI, better decisions, and faster delivery. Our customer Business and engineering teams collaborate in one workspace to design, build, and ship smarter enterprise apps quickly, turning complex connected data into clear outcomes.

We are looking for a Senior Implementation Lead to lead complex AI Fabric implementations in EMEA leveraging Knowledge Graphs, Generative AI, and the Siemens technology suite.

• Lead end‑to‑end implementations of Rapid Miner Graph Studio and Graph Lakehouse solutions, transforming enterprise data into strategic knowledge graph platforms
• Architect and deploy semantic data integration solutions using W3C standards (RDF, RDFS, OWL, SPARQL) to create enterprise‑scale knowledge graphs for critical business applications
• Design and implement ontology models that represent complex business domains, enabling semantic interoperability and advanced analytics across disparate data sources
• Build semantic mapping frameworks to transform structured and unstructured data sources into RDF triple stores, ensuring data quality and consistency
• Configure and optimize Graphmart architectures including query templates, validation rules, search indexes, write‑back mechanisms, and inference engines
• Integrate authentication and authorization frameworks (Keycloak, ABAC) with fine‑grained access control policies for secure knowledge graph deployments
• Implement data virtualization strategies to provide unified semantic access layers across federated data sources without physical data movement
• Develop SPARQL queries and interactive dashboards using Harris Analytics to deliver actionable insights from knowledge graph data
• Deploy GenAI applications integrated with knowledge graphs to enable retrieval‑augmented generation (RAG), semantic search, and AI‑powered analytics
• Partner with data architects and engineers to design scalable graph database architectures aligned with enterprise data governance frameworks
• Mentor customer teams on knowledge graph best practices, semantic modeling methodologies, and Graph Studio platform capabilities
• Lead workshops and enablement sessions for partners and customers, accelerating adoption and building internal competencies
• Troubleshoot complex technical challenges in production environments, providing rapid resolution and continuous optimization
• Collaborate with product teams to provide customer feedback, influence roadmap priorities, and contribute to platform evolution
• Support pre‑sales activities by conducting technical discovery, proof‑of‑concept implementations, and solution architecture design
• Champion AI and knowledge graph adoption within customer organizations by demonstrating value through tangible business outcomes
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Education:
• A degree in Computer Science, Data Science, Information Systems, Software Engineering, or related technical field/
• Advanced certifications in semantic web technologies, knowledge graphs, or enterprise data architecture are a plus.
Experience &

Skills:
• 8+ years of experience in enterprise software implementation, with at least 4+ years focused on AI, knowledge graphs, semantic technologies, or advanced analytics platforms
• Proven expertise in knowledge graph platforms, semantic web standards (RDF, RDFS, OWL, SPARQL, SHACL), and graph database technologies (e.g., RDF triple stores, property graphs)
• Deep understanding of ontology engineering, semantic modeling, and linked data principles with hands‑on experience creating production‑grade ontologies
• Demonstrated success leading complex, multi‑stakeholder implementation projects from requirements gathering through production deployment
• Strong technical skills in data integration, ETL/ELT processes, and working with diverse data sources (relational databases, No
  
  SQL, cloud storage, APIs)
• Experience with GenAI technologies including large language models, vector databases, embeddings, and retrieval‑augmented generation (RAG)…