data ecosystems ( AWS, Azure, GCP ). Strong communication and stakeholder engagement skills — translating complex technical concepts into business outcomes. 💡 Bonus Points For Experience with semantic knowledge graphs , RDF/SPARQL , and ontology/taxonomy modeling . Familiarity with metadata-driven AI/ML enrichment . Knowledge of financial mathematics or capital markets. A “can-do” mindset and hunger for continuous More ❯
data ecosystems ( AWS, Azure, GCP ). Strong communication and stakeholder engagement skills — translating complex technical concepts into business outcomes. 💡 Bonus Points For Experience with semantic knowledge graphs , RDF/SPARQL , and ontology/taxonomy modeling . Familiarity with metadata-driven AI/ML enrichment . Knowledge of financial mathematics or capital markets. A “can-do” mindset and hunger for continuous More ❯
XML, or other structured data formats. Experience with rules engines, decision management systems, or low-code/BPM tools. Understanding of knowledge representation, logic, or semantic technologies (RDF, OWL, SPARQL). Awareness of machine learning and how probabilistic and deterministic reasoning can complement each other. Domain experience in financial services, insurance, healthcare, or other regulated industries. Degree in a logic More ❯
City of London, London, United Kingdom Hybrid/Remote Options
Intelix.AI
ontology design across domains (assets, risk, supply chain, compliance) Build ingestion pipelines (ETL/streaming/CDC) and entity resolution for graph population Author complex queries (Cypher, GSQL, AQL, SPARQL etc. depending on stack) Integrate knowledge graph retrieval & reasoning into LLM/RAG/GraphRAG systems Develop and evaluate graph ML/embedding models (link prediction, anomaly detection) Optimize graph … 5+ years in engineering, data, or AI roles Deep experience with at least one graph technology: Neo4j, TigerGraph, ArangoDB, OrientDB, or Stardog Proficiency in query languages (Cypher, GSQL, AQL, SPARQL, etc.) Strong background in pipelines, ETL, and entity resolution Exposure to integrating KG + LLM or RAG architectures Experience with graph algorithms, embeddings, or GNNs Cloud & production engineering literacy (AWS More ❯
ontology design across domains (assets, risk, supply chain, compliance) Build ingestion pipelines (ETL/streaming/CDC) and entity resolution for graph population Author complex queries (Cypher, GSQL, AQL, SPARQL etc. depending on stack) Integrate knowledge graph retrieval & reasoning into LLM/RAG/GraphRAG systems Develop and evaluate graph ML/embedding models (link prediction, anomaly detection) Optimize graph … 5+ years in engineering, data, or AI roles Deep experience with at least one graph technology: Neo4j, TigerGraph, ArangoDB, OrientDB, or Stardog Proficiency in query languages (Cypher, GSQL, AQL, SPARQL, etc.) Strong background in pipelines, ETL, and entity resolution Exposure to integrating KG + LLM or RAG architectures Experience with graph algorithms, embeddings, or GNNs Cloud & production engineering literacy (AWS More ❯
Design and develop a scalable modular semantic layer framework, which can be seamlessly integrated with various data and artificial intelligence (AI) and machine learning (ML) components across the organization. Create logical and physical data models, ensuring data consistency, integrity, and More ❯
