6 of 6 SPARQL Jobs in London

with dbt Labs Good Python skills, API integration, automation Practical Snowflake and Databricks experience including Cortex AI and Genie Working knowledge of OWL, RDF, SPARQL and enterprise ontology design Experience with at least one agentic AI framework GitHub Copilot or equivalent AI coding assistant experience in a professional setting Financial ...

toolset)Proven experience with dbt LabsGood Python skills, API integration, automationPractical Snowflake and Databricks experience including Cortex AI and GenieWorking knowledge of OWL, RDF, SPARQL and enterprise ontology designExperience with at least one agentic AI frameworkGitHub Copilot or equivalent AI coding assistant experience in a professional settingFinancial services background preferred ...

analysis. Hands on technical strength in Python and SQL, with working command of the modern data stack and of graph query languages such as SPARQL, Cypher or equivalent & strong knowledge of data surface infrastructures such as Snowflake/Databricks etc. Practical experience designing knowledge graph, ontology or linked data infrastructure ...

modeling)Ontology Engineering (OWL/RDF, Protégé)Graph RAG (LLM integration, retrieval strategies)API Design (REST/GraphQL)Preferred SkillsGraph platforms: Neptune, TigerGraphSemantic standards: SPARQL, SHACLAI/ML and LLM ecosystem exposureData governance and integration experienceSummaryThis role is pivotal in shaping the semantic foundation for enterprise AI, bridging data architecture ...

extensible knowledge graph solutions for enterprise applications.Expertise in ontology engineering, semantic modeling, taxonomy design, and enterprise knowledge representation.Deep understanding of graph databases, RDF, OWL, SPARQL, property graphs, linked data, and semantic interoperability.Experience designing and implementing GraphRAG, Hybrid RAG, contextual retrieval, vector search, semantic search, and enterprise knowledge retrieval architectures.Experience integrating ...

both — you know what's worth automating and why. Comfortable working directly with a Python codebase — building, debugging, and extending features. Graph literacy — Neo4j, SPARQL, or equivalent. Can inspect a knowledge graph and judge its fitness. Agent fluency — has built LLM agents in production. Knows what goes wrong. Strong communicator ...