26 to 37 of 37 Ontologies Jobs in London

engineering, generative AI, LLM, and multi-modal models; look for opportunities to apply them to the problem at hand. You design, evaluate, and maintain ontologies as needed. You help articulate the value of generative AI and knowledge graph approaches for a given business problem. You share what they learn with … degree or equivalent, plus at least 3 of the following: Experience with Knowledge Graph technologies (RDF, SPARQL, LPG, SHACL) Experience in schema design, ontology management, and KG curation Expertise in designing and developing KG solutions and graph-based ML models (functional + technical) Experience with end‐to‐end data pipeline ...

define what staying at the forefront means for the practice; drive innovation and translate it into new offerings. You set the standards for how ontologies are designed, evaluated, and maintained across programs. You make the case to senior stakeholders and shape the methodologies and data architectures that prove value … equivalent, plus all of the following: Significant experience with Knowledge Graph technologies (RDF, SPARQL, LPG, SHACL). Advanced practical knowledge of schema design, ontology management, and KG curation. Seasoned in designing and developing KG solutions and graph-based ML models (functional + technical). Proven experience ...

into the team’s roadmap. Contribute to knowledge graph and semantic enrichment efforts that support retrieval systems. Domain & Research Integration Collaborate with domain experts, ontology engineers, and biomedical informaticians to integrate scientific taxonomies, citation networks, and clinical ontologies into retrieval systems. Incorporate structured data — including datasets, chemical entities, genes, drugs … Computer Science, Data Science, Machine Learning, or a related field Experience working with large-scale datasets (scientific, biomedical, or enterprise data) Familiarity with scientific ontologies and metadata standards (e.g., MeSH, UMLS, ORCID, CrossRef) Exposure to production ML systems and MLOps practices Familiarity with data visualization and analytical tooling (e.g., Tableau ...

integrate emerging technologies into the team’s roadmap. Contribute to knowledge graph and semantic enrichment efforts that support retrieval systems. Collaborate with domain experts, ontology engineers, and biomedical informaticians to integrate scientific taxonomies, citation networks, and clinical ontologies into retrieval systems. Incorporate structured data — including datasets, chemical entities, genes, drugs … Computer Science, Data Science, Machine Learning, or a related field Experience working with large‐scale datasets (scientific, biomedical, or enterprise data) Familiarity with scientific ontologies and metadata standards (e.g., MeSH, UMLS, ORCID, CrossRef) Exposure to production ML systems and MLOps practices Familiarity with data visualization and analytical tooling (e.g., Tableau ...

stronger cross-sell. Deliver in stages, proving value within 90 days, with reporting continuity guaranteed throughout. Master Data & Shared Definitions:Define and maintain the ontologies, taxonomies, and reference data that QIMA's systems currently define differently. Sequence the work by business urgency and risk, not by theoretical completeness. Name … sentence, what business outcome it unlocks - and you say no to the ones that can't. Data Governance & MDM:Hands-on experience defining data ontologies, taxonomies, master data strategies, and data contracts, taking an organization from fragmented, system-specific definitions to one shared standard. Cross-Functional Leadership & Influence:Strong track ...

contribute recommendations for experimentation and adoption. Contribute to prompt engineering, grounding strategies, context management, and hallucination mitigation efforts. Support integration of scientific metadata, ontologies, and knowledge assets into AI-powered workflows. Search, Retrieval & RAG Systems Design, develop, and optimize search and retrieval pipelines , including lexical, vector, and hybrid retrieval approaches. … conversational AI applications Experience working on search, ranking, recommendation, or retrieval systems Familiarity with scientific, biomedical, or scholarly datasets Experience with knowledge graphs, ontologies, or semantic enrichment systems Exposure to production ML systems and MLOps practices Academic or industry research experience in NLP, information retrieval, search, or generative AI Experience ...

contribute recommendations for experimentation and adoption. Contribute to prompt engineering, grounding strategies, context management, and hallucination mitigation efforts. Support integration of scientific metadata, ontologies, and knowledge assets into AI-powered workflows. Search, Retrieval & RAG Systems Design, develop, and optimize search and retrieval pipelines , including lexical, vector, and hybrid retrieval approaches. … conversational AI applications Experience working on search, ranking, recommendation, or retrieval systems Familiarity with scientific, biomedical, or scholarly datasets Experience with knowledge graphs, ontologies, or semantic enrichment systems Exposure to production ML systems and MLOps practices Academic or industry research experience in NLP, information retrieval, search, or generative AI Experience ...

contribute recommendations for experimentation and adoption. Contribute to prompt engineering, grounding strategies, context management, and hallucination mitigation efforts. Support integration of scientific metadata, ontologies, and knowledge assets into AI‐powered workflows. Design, develop, and optimize search and retrieval pipelines, including lexical, vector, and hybrid retrieval approaches. Contribute to the development … conversational AI applications. Experience working on search, ranking, recommendation, or retrieval systems. Familiarity with scientific, biomedical, or scholarly datasets. Experience with knowledge graphs, ontologies, or semantic enrichment systems. Exposure to production ML systems and MLOps practices. Academic or industry research experience in NLP, information retrieval, search, or generative AI. Experience ...

package what works into reusable patterns, components, and technical narratives that scale across GTM and delivery ecosystems. Responsibilities Design and build knowledge graphs and ontologies that underpin Mendix's AI solutions, technical demos, and customer implementations. Own the data and graph architecture workstream within integrated team solutions, working alongside Solutions … analyst briefings. Requirements Demonstrable hands-on experience designing and building knowledge graphs and ontologies. Strong understanding of graph data modelling, semantic modelling, and ontology design. Experience with graph database platforms and query languages (e.g. SPARQL, Cypher, or equivalent). Data engineering fundamentals: pipelines, data preparation, and integration with enterprise systems ...

concept knowledge graph modeling investment fund data for a leading asset manager. The ideal candidate will possess extensive experience with RDF/OWL ontologies and related validation techniques. The role involves coding business rules into machine-readable formats to streamline fund data processes and enhance automation across global markets. Exposure ...

will bridge the gap between complex biomedical data and actionable AI applications. You will conduct comprehensive health checks on our current stack, refine bio-ontologies, and build the strategic roadmap for our next-generation GraphRAG and GenAI platforms. Key responsibilities - Platform Assessment: Conduct comprehensive technical health checks on existing graph … databases and cluster infrastructure. Ontology & Schema Review: Evaluate RDF/OWL and Labeled Property Graph (LPG) schemas for enterprise scalability. Standards Alignment: Map internal data frameworks to biomedical standards like MeSH, SNOMED, and UMLS. Performance Optimization: Eliminate data bottlenecks across real-time ingestion pipelines and complex query execution. Strategic Roadmap ...

function. The Head of AI is responsible for building and leading a team that builds production systems handling unstructured clinical text, structured knowledge (ontologies and graphs), and generative AI. Dyad has a learning and teaching culture and the candidate for the role should be as comfortable coming up with accessible … frameworks for: Hallucination detection Precision and recall of extracted clinical concepts Regression testing across model updates Implement structured output approaches (e.g. schema-constrained generation, ontology-driven formats). Design iterative feedback loops, including human-in-the-loop review where appropriate. Ensure measurable improvements in grounding, explainability, and reliability over time. ...