26 to 50 of 53 Ontologies Jobs in the UK

# Semantic Governance Managing Consultant - Ontologies and SemanticsBirmingham, Bristol, London, Manchester, Newcastle Bank HouseApply for this job* Permanent* Experienced Professionals* Data & AI* ID 529956-en\_GB## **Who You'll Be Working With**You will join the Data Trust Capability in Capgemini’s Insights and Data (I&D) business unit. Insights … data assets.This role will be primarily focused on delivering the Semantic Layer Programmes, supporting the development of supporting the design and adoption of enterprise ontologies, semantic standards, controlled vocabularies and knowledge graph capabilities across complex client environments.**PLEASE NOTE:****Security Clearance: To be successfully appointed to this role, must ...

fund data for one of the world's largest asset managers. You will encode business rules, regulatory constraints, and identifier relationships as machine‐readable ontologies — directly eliminating manual fund data processes and enabling automated validation across thousands of funds in 6 global markets. Defined POC scope, executive sponsorship, and weekly … technical guidance from two of the most recognized knowledge graph practitioners in financial services. What You’ll Deliver (POC Scope) An investment fund ontology (OWL 2/RDF/Linked Data) modeling the product hierarchy: Fund > ShareClass > Listing > Security, with 73+ identifier types (ISIN, SEDOL, CUSIP, LEI, Bloomberg, and more ...

high‐throughput, low‐latency inferencing and classical ML deployment within unified, production‐ready platforms Own the architecture of the enterprise context layer—knowledge graphs, ontologies, vector search, and semantic retrieval—grounding the solution in client knowledge Set the design direction for context assembly and memory that manages prompts, context windows ...

time-series and multimodal data, simulation and world models, trustworthy decision-support agents and staged autonomy, and AI-native approaches to industrial data foundations, ontology learning, and evaluation. This is a hands‐on role for someone who thrives in uncertain problem spaces, works with high autonomy, and finds practical ways ...

Data-as-a-Service as a real product — a secure, multi-tenant, immutable release architecture, with data made discoverable at scale through catalogue and ontology-driven search, in partnership with the data team. Powering agentic AI from the data platform — scaling the data foundations behind MystraAI and owning secure, tenant ...

data assets.This role will be primarily focused on delivering the Semantic Layer Programmes, supporting the development of supporting the design and adoption of enterprise ontologies, semantic standards, controlled vocabularies and knowledge graph capabilities across complex client environments.PLEASE NOTE:Security Clearance: To be successfully appointed to this role, must be eligible … Standards, Controlled Vocabularies, Data Contracts, Semantic Data Products and Knowledge Graph implementations.This is a senior client-facing leadership role requiring deep technical expertise in ontology engineering, semantic technologies and knowledge representation, combined with the ability to engage senior stakeholders and facilitate consensus across business and technology teams.The successful candidate will ...

enterprise data architectures, cloud data platforms, and data integration strategies.* Information Architecture: Establishes the enterprise-wide business glossary, conceptual data blueprints, and taxonomy/ontology frameworks to ensure a unified understanding of data across the business.* Data Modeling: Oversees the creation and maintenance of enterprise conceptual, logical and physical data … pipelines.* Information Architect + Deep expertise in metadata management, master data management (MDM), and reference data management. + Proven ability to construct enterprise taxonomies, ontologies, data lineages, and business glossaries. + Strong understanding of semantic web technologies (RDF, OWL, Knowledge Graphs).* Data Modeler + Expert proficiency in industry-standard ...

enterprise data architectures, cloud data platforms, and data integration strategies. Information Architecture: Establishes the enterprise-wide business glossary, conceptual data blueprints, and taxonomy/ontology frameworks to ensure a unified understanding of data across the business. Data Modeling: Oversees the creation and maintenance of enterprise conceptual, logical and physical data … secure data pipelines. Information Architect Deep expertise in metadata management, master data management (MDM), and reference data management. Proven ability to construct enterprise taxonomies, ontologies, data lineages, and business glossaries. Strong understanding of semantic web technologies (RDF, OWL, Knowledge Graphs). Data Modeler Expert proficiency in industry-standard data modeling ...

tune – Codify the firm's unwritten methods and judgements into the coworkers and playbooks that run at machine speed. Anchor them through the ontology and semantic layers in Celeste so the agents reason the way its own people do. Earn adoption – Lead the actions that earns Celeste's adoption, from ...

LLMs, retrieval‐augmented generation (including hybrid retrieval across semantic, graph and keyword search, with reranking and embedding‐model selection), knowledge bases, knowledge graphs and ontologies, agentic workflows and skills, evaluation methods (eval harnesses, LLM‐as‐judge, groundedness and faithfulness metrics, tracing), and model risk controls. Experience building secure, scalable … closely with subject matter experts to refine AI tools iteratively and turn specialist feedback into practical product improvements. Strong grasp of structured data, taxonomies, ontology‐style data models and mappings across complex standards or schemas. Experience with user testing, controlled pilots, adoption planning, training and feedback loops for specialist user ...

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, an... WKCL1_UKTJ ...

NIST AI RMF and enterprise governance standards Data, Semantic & Knowledge Architecture Support development of enterprise semantic and knowledge capabilities Define architecture approaches for: enterprise ontologies semantic models metadata-driven platforms knowledge graph integration unstructured data processing Ensure trusted and governed data foundations underpin AI-enabled capabilities Collaborate with data architecture … operational teams Experience influencing senior stakeholders within matrixed organisations Technical skills AI orchestration and agentic workflow architectures RAG and GraphRAG architectures Knowledge graphs, ontologies and semantic technologies Vector databases and semantic retrieval patterns Microsoft Fabric, Azure AI and modern cloud platforms API and integration architecture Enterprise security and identity models ...

technical expectations. Customer Advisory: Provide remote and on-site technical advisory services to customers, offering guidance on data attributes, integration strategies, and system optimization. Ontology & Model Development: Advise on the evolution of Janes data models and ontologies to ensure compatibility and seamless integration with customer ecosystems. Gap Analysis: Identify ...

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 ...

public sector) Embed client strategy and technical teams Stretching the frontier: hybrid KG + AI, graph + reasoning + M 🛠 Responsibilities Lead KG schema & ontology design across domains (assets, risk, supply chain, compliance) Build ingestion pipelines (ETL/streaming/CDC) and entity resolution for graph population Author complex queries …/AI concepts to non-technical audiences ✅ Nice-to-Have/Bonus Assets Experience with GraphRAG or KG-backed LLM retrieval Semantic web/ontology skills (RDF/OWL/SHACL) Prior consulting or client delivery background Graph visualization/UI experience (Linkurious, Bloom, Ogma) Graph DB certifications (Neo4j, Stardog ...

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 ...