1 to 25 of 34 Ontologies Jobs in England

experience or demonstrable ability to upskill quickly in new platforms.#LI-MW2Skills: Amazon Web Services CloudApache SparkDatabricksETLMicrosoft SQL ServerPythonData GovernanceData ModelingPalantir Foundry Code WorkbookPalantir Foundry Ontology MgrPalantirFoundryPipelineBuilderWhat you can expect from us: Together, as owners, let's turn meaningful insights into action. Life at CGI is rooted in ownership, teamwork, respect ...

modelling. Strong stakeholder engagement and influencing skills, with the ability to communicate complex concepts to technical and non-technical audiences Experience developing semantic models, ontologies, taxonomies, business glossaries or knowledge structures. Strong knowledge of metadata, data lineage, data quality, master data management and data governance. Experience defining enterprise architecture principles ...

Doing Designing and developing scalable solutions within Palantir Foundry Building data pipelines, data models and data integration solutions Developing and maintaining Foundry Ontologies Creating high-quality, governed data assets for operational, analytical and AI use cases Building user-facing applications using low-code and pro-code capabilities Working directly with ...

Doing Designing and developing scalable solutions within Palantir Foundry Building data pipelines, data models and data integration solutions Developing and maintaining Foundry Ontologies Creating high-quality, governed data assets for operational, analytical and AI use cases Building user-facing applications using low-code and pro-code capabilities Working directly with ...

Deployment or promotion through environments. Operational support and troubleshooting. Experience dealing with: Performance issues or optimisation tasks. Permissions and basic security models. Schema/ontology changes and their impact on downstream use cases. Integration problems and incident recovery in collaboration with senior staff. Some client-facing experience (e.g. workshops, demos ...

will help define and drive the capabilities, patterns and practices needed to make enterprise data AI-ready, including semantic layers, knowledge graphs, and ontologies to ensure trusted, governed and scalable use of enterprise data by AI applications and agents. The successful candidate will combine strong knowledge of AI and modern ...

semantic technology. We care more about what you have built and how you approach a problem than a particular tool stack or formal ontology background. You have built a knowledge graph or another connected data application for analytics, content or AI, and can take an idea from domain conversations ...

level stakeholders and aligning technical outcomes with business valueNice to haveExperience applying AI in drug discovery, safety monitoring or clinical development contextsUnderstanding of biomedical ontologies or knowledge graph applicationsKnowledge of regulatory frameworks (FDA, EMA) and experience with data privacy considerationsBackground in computational biology, biostatistics or related scientific disciplineFamiliarity with federated ...

secure and business aligned.Partner with data and analytics engineering teams to design AI-ready data architectures, including data structures, metric layers, semantic models and ontologies, while developing and evaluating proof of concepts to validate emerging technologies, architectural patterns and AI capabilities.Define and champion enterprise-wide standards, governance and best practices ...

Data – Partner with dashboard product owners to shape their dashboards into an AI-ready form the agent can query cleanly and reliably. Semantic Layers & Ontologies – Contribute to semantic models (metrics, definitions, and metadata) so a given metric means the same thing everywhere it's asked about, however the question ...

ability to engage effectively with technical teams. Hands on experience with technical tooling and code development experience Knowledge of semantic layers, graphs, and ontologies Experience and curiosity around AI-powered development. Ability to translate business needs into clear data design, with a focus on enabling decision-making and delivering measurable ...

semantic models, metadata expectations and architecture decision records. Promote consistent information models, data contracts, naming, lineage and contextualization practices, including alignment with enterprise ontology, knowledge-graph and unified namespace approaches where these support approved business outcomes. Establish measures for adoption, reuse, reliability, data quality, service performance, user experience and benefits ...

silos and turning it into knowledge that people and machines can meaningfully understand. Graph and Semantics Engineering are emerging enterprise data technologies that use ontologies, advanced analytics and AI to represent knowledge in expressive and innovative ways. Next-generation AI systems powered by knowledge graphs can help organisations manage complexity ...

complementary patent resource containing chemical structures extracted from patents daily. UniChem links chemical structures across databases. ChEBI is a highly curated database and ontology of compounds of biological interest. OPSIN converts systematic chemical names into chemical structures. The EBI is part of the European Molecular Biology Laboratory (EMBL ...

complementary patent resource containing chemical structures extracted from patents daily. UniChem links chemical structures across databases. ChEBI is a highly curated database and ontology of compounds of biological interest. OPSIN converts systematic chemical names into chemical structures. The EBI is part of the European Molecular Biology Laboratory (EMBL ...

agent/LLM evaluation (eval datasets, automated and expert-in-the-loop scoring, regression testing). Experience with API integration, retrieval layers, knowledge graphs, ontologies, or semantic search. Daily use of AI coding agents (Claude Code, GitHub Copilot, Databricks AI Assistant) as the primary mode of development. Preferred Experience Prior ...

multidisciplinary and multi-location teams. Set yourself apart: Relevant experience in areas such as Agentic Data Management, AI Observability, semantic layers, knowledge graphs and ontologies Relevant certifications e.g. DAMA CDMP, DCAM, technology or cloud accreditations Implementation experience with leading technologies in data governance, catalogue, Data Quality, observability, MDM or cloud ...

experience working with medical imaging datasets, including DICOM or similar formats. Desirable: Experience with DICOM or other medical imaging standards. Knowledge of image ontologies, classification or biomedical data structures. Previous experience within life sciences, healthcare, scientific research or medical technology. The Benefits Opportunity to work on a specialist data engineering ...

ability to explain technical decisions and assess different architectural approaches. Confidence taking ownership of projects and guiding other engineers technically. Experience with knowledge graphs, ontologies, recommendation systems, human-in-the-loop learning or LLM fine-tuning would be particularly interesting. You don't necessarily need to have held an Architect ...

data, AI and process challenges into scalable Foundry applications. Key responsibilitiesAct as lead architect for Foundry, owning solution design from ingestion and pipelines through Ontology, applications and AI use cases. Translate business problems into Foundry use cases, technical designs and deliverable roadmaps. Design and oversee data pipelines, Ontology models, security … Skills and experienceSignificant hands-on experience delivering Palantir Foundry solutions in complex client environments. Deep Foundry technical expertise across the full stack: Pipeline Builder, Ontology, Workshop, OSDK, Code Repositories, Actions and AIP or agentic capabilities, able to build production-grade applications not just prototypes. Strong proficiency in at least ...

differentiated career path that is difficult to develop in a purely technology-focused role. Purpose of the roleResponsible for the finance data and ontology layer on which agentic solutions depend. Builds the entities, relationships, definitions, lineage and data contracts sourced from live ERP and EPM systems to a standard that … retrieval-ready data services that are secure, permission-aware, observable and suitable for AI, analytics and decision intelligence. ResponsibilitiesBuild and extend the finance ontology covering supplier, invoice, customer, receivable, journal, account, cost centre and forecast line, and the relationships between them, including controlled vocabularies, business glossaries, hierarchy history, ownership ...

differentiated career path that is difficult to develop in a purely technology-focused role. Purpose of the roleResponsible for the finance data and ontology layer on which agentic solutions depend. Builds the entities, relationships, definitions, lineage and data contracts sourced from live ERP and EPM systems to a standard that … retrieval-ready data services that are secure, permission-aware, observable and suitable for AI, analytics and decision intelligence. ResponsibilitiesBuild and extend the finance ontology covering supplier, invoice, customer, receivable, journal, account, cost centre and forecast line, and the relationships between them, including controlled vocabularies, business glossaries, hierarchy history, ownership ...

leadership to enable attack-path analysis, threat investigation, exposure prioritization, GraphRAG, and AI-assisted security operations. Key Responsibilities Design the Security Knowledge Graph architecture, ontology, taxonomy, entity model, relationship model, provenance model, and lifecycle standards. Build production-grade graph ingestion and transformation pipelines for SIEM, EDR/XDR, IAM/… engineers while remaining accountable for prototypes, reference implementations, critical code, troubleshooting, and complex customer or stakeholder demonstrations. Mandatory Hands-on Technical Skills Knowledge graphs: Ontology and semantic model design; property graphs and RDF; graph schema evolution; knowledge representation; provenance; graph quality; entity and relationship resolution. Graph platforms: Deep implementation experience ...

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 ...

Senior Data Architect - Data Platform - Data Modeling - MDM - Data Management - Azure - Databricks As a Senior Data Architect, you will align the organisation's data architecture with its overall data strategy, establishing robust standards, governance and ...