1 to 25 of 71 Permanent Ontologies Jobs in London

usability. Valuable additional experience Production AI/ML or generative-AI data architecture, including RAG, embeddings, vector search or model-data pipelines. Knowledge graphs, ontologies, RDF, linked data or advanced semantic technologies. Databricks or cloud architecture/data-engineering certification. Tools such as dbt, Kafka, Airflow, Terraform, Power BI, Tableau ...

Lake, Azure Data Factory, dbt, Airflow, MLflow, Power BI, Microsoft Purview or comparable tools. Experience with search indexes, vector databases, graph databases, knowledge graphs, ontologies or master-data-management techniques. Experience with MLOps, model monitoring, experimental design, causal inference, forecasting, recommendation systems or optimisation. Experience with legal, financial, client, market ...

lineage and constraintsEnsure requirements include data quality, metadata and governance needs (e.g. definitions, business rules, reference data, retention)Contribute to and consume business glossaries, ontologies and data catalogues to ensure consistent terminology and understandingHelp ensure AI use cases comply with data privacy, security and regulatory requirements (e.g. GDPR, sector specific ...

data integration, analytics, and data security. Advanced knowledge of conceptual, logical, and physical data modelling. Experience producing architecture artefacts including ERDs, data dictionaries, taxonomies, ontologies, and enterprise data models. Strong understanding of data management disciplines including master data management and metadata management. Experience working with relational databases, APIs, transactional systems ...

including large-scale, incremental, and change-data ingestion. Experience with enterprise content ecosystems: SharePoint, ideally Power Platform/Power Automate. Knowledge representation, taxonomies/ontologies, and versioned/temporal data. Understanding of how data structures feed RAG and LLM retrieval quality. SQL proficiency; experience with orchestration tools (e.g., Airflow ...

platform evolution by onboarding new sources, refining deployment templates/workflows, and mentoring engineers on engineering standards. Designing and maintaining a business data ontology with canonical entities, relationships, and shared vocabulary. Stakeholder Engagement & Communication Build and maintain trusted relationships with business stakeholders, data product owners, and reporting teams – acting ...

maintain knowledge graph structures that reflect canonical domain models. Develop and manage graph database schemas, queries, and data ingestion pipelines. Ensure semantic consistency between ontology definitions and downstream data products. AI & Vector Infrastructure Design and implement embedding pipelines that represent Comply’s financial and regulatory data in vector space. Build ...

Schema & Pipeline Engineering: Develop and maintain end-to-end multi-stream ingestion pipelines for complex clinical trial schemas. Use AI-driven schema inference and ontology alignment tools to auto-draft mapping artifacts, dramatically reducing integration timelines across different life science datasets. High-throughput Streaming & Backend Services: Build scale, fault-tolerant ...

maintain knowledge graph structures that reflect canonical domain models • Develop and manage graph database schemas, queries, and data ingestion pipelinesEnsure semantic consistency between ontology definitions and downstream data product AI & Vector InfrastructureDesign and implement embedding pipelines that represent Comply's financial and regulatory data invector spaceBuild and operate vector database ...

schemas, and project lifecycles (Agile preferred)Bonus Points ForHands-on experience with data modelling tools such as Erwin, Sparx, Visual ParadigmExperience working with ontologies and knowledge of RDF/OWL/TTLExposure to Python or SQL scripting in support of data model designKnowledge of data governance and enterprise metadata integration ...

data discovery platforms Familiarity with semantic web or knowledge graph concepts (e.g., RDF, SKOS, OWL) and experience integrating these into usable data models. Formal ontology design is not required, but ability to collaborate with ontologists is essential Understanding of data governance frameworks or certifications (e.g., DAMA CDMP, DCAM) Exposure ...

analytics platform. E.g from functional genomics and perturbation screens, molecular profiling and expression atlases, genomic and functional annotations, and curated interaction, pathway and ontology resources. Implement production quality code for processing, transforming and quality control of new data types for integration into our larger data model Contribute to and implement ...

closely with engineering, data, and AI teams to clarify requirements, unblock delivery, and ensure platform quality Define and enforce governed taxonomies, metadata models, and ontologies to ensure findability, consistency, and AI-readiness across knowledge assets Establish data contracts between knowledge producers and consumers, capturing lineage, quality standards, source ownership ...

aligning technical outcomes with business value; Nice to have: Experience applying AI in drug discovery, safety monitoring, or clinical development; understanding of biomedical ontologies or knowledge graph applications; knowledge of FDA and EMA regulatory frameworks and data privacy considerations; background in computational biology, biostatistics, or a related scientific discipline; familiarity ...

other cloud-native platforms. Event-driven architectures and asynchronous messaging. Istio, mTLS or service mesh technologies. Knowledge of semantic search, knowledge graphs or ontology-based solutions. Mandatory Requirements Current DV Security Clearance. Ability to work onsite at an MOD location in London four days per week. Previous experience delivering within ...

aligning technical outcomes with business value Nice to have Experience applying AI in drug discovery, safety monitoring or clinical development contexts Understanding of biomedical ontologies or knowledge graph applications Knowledge of regulatory frameworks (FDA, EMA) and experience with data privacy considerations Background in computational biology, biostatistics or related scientific discipline ...

remediation backlogDefine and enforce remediation SLAsCollaborate with engineering and domain teams to resolve quality issuesEstablish governance processes for ongoing quality assuranceCollaborationPartner with data engineering, ontology, and AI teamsAlign quality standards with enterprise data governance frameworksPromote best practices in data quality management and validationRequired SkillsData Quality Frameworks (profiling, validation, governance)Graph ...

catalogue initiatives. Able to produce client-ready architecture documents and written review responses without editorial support. Will be a plus: Knowledge of financial-industry ontologies (e.g. FIBO or similar) and a realistic view of their practical limitations. Familiarity with semantic search over metadata based on embeddings. Domain experience in asset ...

discovery, knowledge extraction, decision support, and intelligent content understanding. Work with large-scale, complex, and heterogeneous data, including scientific publications, research datasets, knowledge graphs, ontologies, taxonomies, citations, metadata, and content from every scientific discipline. Apply the right technique to each problem, using approaches such as classification, regression, clustering, ranking, feature ...

payroll feeds via SFTP and in EDI format Experience with MCP server development , or different ways of preparing data for AI agents such as ontology modelling, context, knowledge graphs, and semantic layers. We don't expect all of it. We do expect curiosity about it. Why join ...

target architecture, governance, and technology roadmap. You will lead interviews with business and tech teams, document data flows, assess data quality, and contribute to ontology design for AI/analytics while using Python and LLMs to automate analysis where appropriate. #J-18808-Ljbffr ...

will: Own large areas of platform end to end, driving components from design through to production deployment. Work on knowledge representation systems, including ontologies and knowledge graphs, to support structured reasoning over enterprise data. Design and implement RAG pipelines, including chunking, embedding, indexing, retrieval, and reranking. Build and maintain integrations ...

will: Own large areas of platform end to end, driving components from design through to production deployment. Work on knowledge representation systems, including ontologies and knowledge graphs, to support structured reasoning over enterprise data. Design and implement RAG pipelines, including chunking, embedding, indexing, retrieval, and reranking. Build and maintain integrations ...

What you'll doOwn large areas of the platform end to end, from design through to production deployment. Work on knowledge representation systems, including ontologies and knowledge graphs, to support structured reasoning over enterprise data. Design and implement RAG pipelines, including chunking, embedding, indexing, retrieval, and reranking. Build and maintain ...

person on site Nice to Haves You\'ve done this at a field-heavy data or AI company Experience with knowledge graphs, semantic layers, ontologies, or entity resolution Deployments into regulated or air-gapped environments (financial services, insurance, telco, public sector) Enough Python or SQL to prototype in front ...