8 of 8 Permanent Ontologies Jobs in England

streaming responses, async inference jobs, and robust error/retry semantics. Palantir Foundry Integration Work with Palantir Foundry to build and maintain data pipelines, ontologies, and integrations that connect backend services to Foundry-based analytics and workflows. Leverage Foundry's APIs and tooling to support data modelling, transformation, and secure ...

organisation. Key responsibilities include: Driving enterprise data modelling and governance through the Data Architecture Framework (DAF) Developing conceptual and logical models (UML), taxonomies, ontologies and data glossaries Mapping data flows and establishing lineage across the enterprise ecosystem Supporting and evolving the Microsoft D365 and downstream medallion data architecture Administering ...

with stakeholders across engineering, operations, and leadership. What You Will Be Doing: Design and deliver enterprise-scale solutions on Palantir Foundry Build data pipelines, ontologies, and operational applications Develop data products and decision intelligence tools used day-to-day Translate complex operational problems into scalable technical solutions Contribute to platform … modern data architectures & distributed systems Ability to work in fast-paced, ambiguous environments Excellent stakeholder engagement and communication skills Experience building: Data pipelines & transforms Ontologies and data models Operational workflows/application Desirable Experience: Experience with Palantir AIP or AI-enabled workflows Exposure to LLMs or agentic AI systems Cloud ...

govern enterprise-wide information models for a global organisation. Key Requirements Strong experience with conceptual and logical information modelling Hands-on experience with ontologies, taxonomies, and business semantics Knowledge of RDF, OWL, SKOS and semantic modelling frameworks Experience building and governing enterprise information models, business glossaries, and shared definitions Ability ...

experience of how to use relational databases, content management systems, such as SharePoint Online, enterprise search and AI functionality * Understanding of metadata, taxonomies, ontologies and knowledge graphs and how they can be used to classify and structure data * An aptitude for using technology and be highly computer literate, including strong ...

fund data at one of the world's largest asset managers. You'll encode business rules, regulatory constraints, and identifier relationships into machine-readable ontologies, replacing manual processes and enabling automated validation across thousands of funds in six global markets. The project has a defined scope, executive backing, and guidance … from leading financial services knowledge graph experts. Develop an investment fund ontology (OWL 2/RDF/Linked Data) modelling the hierarchy: Fund > ShareClass > Listing > Security, with 73+ identifier types (eg, ISIN, SEDOL, CUSIP, LEI, Bloomberg). Create SHACL validation shapes to enforce business rules (eg, "A US -domiciled ...

grounded agents, and structured decision-support systems. Work with knowledge-based AI architectures, including retrieval-augmented generation, knowledge graphs, symbolic rules, structured domain models, ontologies, and hybrid reasoning systems. Develop and apply mechanisms for controlling inference, including planning constraints, reasoning policies, guardrails, validation layers, tool-use control, and human … academic research, or ideally both. Hands-on exposure to knowledge-based agentic systems, such as agents grounded in knowledge graphs, structured documents, domain rules, ontologies, databases, or retrieval systems. Experience with methods for controlling reasoning or inference, such as guardrails, constrained planning, validation layers, policy-based tool use, symbolic checks ...

strategy across data harmonisation, pipeline validation, knowledge graph, and FAIR data product requirements. Requirements Translation: Translate ambiguous scientific requirements into clear canonical data models. Ontology Management: Make ontology reuse, extension, and mapping decisions (with documented rationale), and define persistent identifiers and consistent provenance fields. Risk Mitigation: Identify modelling risks early … including metadata gaps, ontology conflicts, source data quality issues, lineage gaps, and downstream compatibility risks. Design & Architecture: Design modular, reusable, and future-proof models aligned to FAIR principles (findability, accessibility, interoperability, reusability) and enterprise standards. Technical Implementation: Apply practical experience with LinkML (or equivalent), including classes, slots, ranges, identifiers, constraints ...