Adecco
Adecco is an employment consultancy. We put expertise, energy, and enthusiasm into improving everyone's chance of being part of the workplace. We respect and appreciate people of all ethnicities, generations, religious beliefs, sexual orientations, gender identities, and more. We do this by showcasing their talents, skills, and unique experience in an inclusive environment that helps them thrive. Are you passionate about transforming enterprise data into meaningful insights? Do you thrive in innovative environments where you can shape the future of data architecture? If so, our client is looking for you! Join us as a Semantic Graph & Ontology Architect and play a pivotal role in developing a Smart Data Fabric that unifies various data sources like Snowflake, SharePoint, and ERP systems, all while enhancing AI capabilities through a sophisticated semantic, graph-native foundation. Role: Semantic Graph & Ontology Architect Duration: 6 Months (extension options) Location: Fully Remote Rate: Competitive (outside ir35) How You'll Make an Impact: As a hands-on leader, you will: Graph & Semantic Architecture: Design scalable graph schemas (LPG and/or RDF/OWL) to meet semantic and inference requirements. Author and optimise queries using Cypher, Gremlin, and SPARQL for seamless data traversal and reasoning. Define canonical entity models and mapping layers to integrate diverse data sources. Ontology Engineering & Reasoning: Create and maintain formal ontologies and taxonomies while governing their versioning and lifecycle. Implement logical inference for agent decision-making and ensure workflow integrity. Establish standards for semantic consistency and data quality checks. Hybrid Semantic Layer (Graph + Logic): Design a hybrid semantic layer that combines graph context with business logic for enhanced search and knowledge contextualization. Model RACI/RBAC as graph edges/nodes, embedding compliance rules for auditability. APIs, Patterns & Collaboration: Define clean API layers for semantic enrichment and retrieval; deliver reference implementations. Collaborate with platform engineers for agent connectivity and tool discovery patterns. Partner with data, platform, and security teams for governance and observability. Quality, Performance & Governance: Set performance budgets to ensure efficient query execution and prevent issues. Establish lineage and governance artefacts like semantic catalogues and audit trails. Document standards and mentor engineers in adopting graph and semantic patterns. What You Bring: A bachelor's or master's degree in computer science, Data Science, Mathematics, Engineering, or a related field. 7-12 years of experience in graph databases, semantic modelling, and ontology engineering. Expertise in query languages like Cypher, Gremlin, and SPARQL, with a strong understanding of LPG vs RDF/OWL tradeoffs. Hands-on experience with Neo4j, AWS Neptune, TigerGraph, or Stardog in a production environment. Proficiency in mapping enterprise data (Snowflake, MongoDB, SharePoint, ERP) into graph and ontology layers. A solid grasp of RBAC/RACI, data governance, lineage, and security controls. Ability to design clean APIs for semantic enrichment and retrieval. Familiarity with AWS services (IAM, VPC, S3, EKS/ECS/Lambda) in collaboration with platform teams. Preferred Qualifications: Experience with ontology tooling (Prot g , SHACL/SWRL) and reasoning engines. Prior delivery of enterprise knowledge graphs supporting workflows and audit trails. Exposure to vector retrieval and how graph context informs data re-ranking. Knowledge of observability tools like OpenTelemetry, Prometheus, and Grafana. Why Join Us? This is your opportunity to be at the forefront of data innovation in the energy sector! If you are eager to make a significant impact and collaborate with talented professionals, we want to hear from you! Apply now and embark on a journey to redefine how data drives decision-making in our client's organisation. Let's build a smarter future together! How to Apply: If you're excited about this opportunity and believe you're a great fit, please answer screening questions during application and submit your CV. Join our client and help shape the future of data engineering! We can't wait to welcome you aboard! Candidates will ideally show evidence of the above in their CV to be considered. Please be advised if you haven't heard from us within 48 hours then unfortunately your application has not been successful on this occasion, we may however keep your details on file for any suitable future vacancies and contact you accordingly. We use generative AI tools to support our candidate screening process. This helps us ensure a fair, consistent, and efficient experience for all applicants. Rest assured, all final decisions are made by our hiring team, and your application will be reviewed with care and attention.
Adecco is an employment consultancy. We put expertise, energy, and enthusiasm into improving everyone's chance of being part of the workplace. We respect and appreciate people of all ethnicities, generations, religious beliefs, sexual orientations, gender identities, and more. We do this by showcasing their talents, skills, and unique experience in an inclusive environment that helps them thrive. Are you passionate about transforming enterprise data into meaningful insights? Do you thrive in innovative environments where you can shape the future of data architecture? If so, our client is looking for you! Join us as a Semantic Graph & Ontology Architect and play a pivotal role in developing a Smart Data Fabric that unifies various data sources like Snowflake, SharePoint, and ERP systems, all while enhancing AI capabilities through a sophisticated semantic, graph-native foundation. Role: Semantic Graph & Ontology Architect Duration: 6 Months (extension options) Location: Fully Remote Rate: Competitive (outside ir35) How You'll Make an Impact: As a hands-on leader, you will: Graph & Semantic Architecture: Design scalable graph schemas (LPG and/or RDF/OWL) to meet semantic and inference requirements. Author and optimise queries using Cypher, Gremlin, and SPARQL for seamless data traversal and reasoning. Define canonical entity models and mapping layers to integrate diverse data sources. Ontology Engineering & Reasoning: Create and maintain formal ontologies and taxonomies while governing their versioning and lifecycle. Implement logical inference for agent decision-making and ensure workflow integrity. Establish standards for semantic consistency and data quality checks. Hybrid Semantic Layer (Graph + Logic): Design a hybrid semantic layer that combines graph context with business logic for enhanced search and knowledge contextualization. Model RACI/RBAC as graph edges/nodes, embedding compliance rules for auditability. APIs, Patterns & Collaboration: Define clean API layers for semantic enrichment and retrieval; deliver reference implementations. Collaborate with platform engineers for agent connectivity and tool discovery patterns. Partner with data, platform, and security teams for governance and observability. Quality, Performance & Governance: Set performance budgets to ensure efficient query execution and prevent issues. Establish lineage and governance artefacts like semantic catalogues and audit trails. Document standards and mentor engineers in adopting graph and semantic patterns. What You Bring: A bachelor's or master's degree in computer science, Data Science, Mathematics, Engineering, or a related field. 7-12 years of experience in graph databases, semantic modelling, and ontology engineering. Expertise in query languages like Cypher, Gremlin, and SPARQL, with a strong understanding of LPG vs RDF/OWL tradeoffs. Hands-on experience with Neo4j, AWS Neptune, TigerGraph, or Stardog in a production environment. Proficiency in mapping enterprise data (Snowflake, MongoDB, SharePoint, ERP) into graph and ontology layers. A solid grasp of RBAC/RACI, data governance, lineage, and security controls. Ability to design clean APIs for semantic enrichment and retrieval. Familiarity with AWS services (IAM, VPC, S3, EKS/ECS/Lambda) in collaboration with platform teams. Preferred Qualifications: Experience with ontology tooling (Prot g , SHACL/SWRL) and reasoning engines. Prior delivery of enterprise knowledge graphs supporting workflows and audit trails. Exposure to vector retrieval and how graph context informs data re-ranking. Knowledge of observability tools like OpenTelemetry, Prometheus, and Grafana. Why Join Us? This is your opportunity to be at the forefront of data innovation in the energy sector! If you are eager to make a significant impact and collaborate with talented professionals, we want to hear from you! Apply now and embark on a journey to redefine how data drives decision-making in our client's organisation. Let's build a smarter future together! How to Apply: If you're excited about this opportunity and believe you're a great fit, please answer screening questions during application and submit your CV. Join our client and help shape the future of data engineering! We can't wait to welcome you aboard! Candidates will ideally show evidence of the above in their CV to be considered. Please be advised if you haven't heard from us within 48 hours then unfortunately your application has not been successful on this occasion, we may however keep your details on file for any suitable future vacancies and contact you accordingly. We use generative AI tools to support our candidate screening process. This helps us ensure a fair, consistent, and efficient experience for all applicants. Rest assured, all final decisions are made by our hiring team, and your application will be reviewed with care and attention.
Thebes IT Solutions Ltd
Role : Ontology Engineer Location: UK Type: Contract Essential Skills: Proven experience in ontology engineering with hands-on OWL, RDF and SKOS delivery in a production or client-facing environment specific semantic ontology and taxonomy experience particularly using tools (Protege and Graphview) Ability to extend and refine existing ontologies, not just build from scratch Strong SPARQL capability including validation and reasoning queries Experience owning and governing taxonomies as versioned, change-controlled assets Understanding of how ontologies and taxonomies feed into AI and RAG systems Strong documentation discipline and ability to record modelling rationale clearly Experience collaborating with technical teams including data engineers and AI developers Highly Desirable: Experience with Protege, TopBraid or equivalent ontology tooling Familiarity with SHACL for constraint validation Background in financial services, private equity or similarly structured enterprise environments Understanding of knowledge grounding principles for large language models Experience with linked data architectures and triple store platforms The Context: Thebes Group is an optimisation company specialising in AI-enabled transformation. We help organisations improve workflow, reporting, information management, and operational decision-making by combining process optimisation, knowledge architecture, semantic technologies, automation, and artificial intelligence. We are currently delivering an AI transformation programme for a private equity group, focused on enhancing group-level operations through intelligent workflows, improved information accessibility, executive reporting, and AI-driven operational intelligence. A foundation ontology and taxonomy already exists. The data is mapped, manageable in scope, and well understood within the team. This is not a build-from-scratch engagement. A Knowledge Graph Architect sits alongside this role to make the semantic structures operational, and an AI engineer handles the agent build. The Ontology Engineer owns the meaning layer: what data is, what the relationships between concepts are, and what the rules are that govern how information should be understood across the organisation. The Role: As Ontology Engineer, you are responsible for the semantic foundation of the programme. You will take the existing ontology and taxonomy and expand, refine and govern them as operational requirements evolve and new agent use cases emerge. Your work defines what the organisation's data means and how concepts relate to each other. That meaning is the input everything else depends on: the knowledge graph, the data pipelines, and ultimately the accuracy of what AI agents know and how they reason. This is a precision role. The quality of your semantic models directly determines the quality of agent outputs across the group. What You Will Do: Expand and maintain the existing ontology as new business requirements emerge, ensuring consistency with the established semantic model Define and refine classes, subclasses, properties, relationships and business rules that accurately represent group-level operational concepts Develop and govern OWL, RDF, RDFS and SKOS artefacts that form the semantic foundation of the programme Own the enterprise taxonomy, managing it as a versioned, governed asset with clear change control and documented rationale Create and maintain SPARQL queries to validate the integrity and consistency of the ontology Work closely with the Knowledge Graph Architect to ensure semantic models translate correctly into graph structures Collaborate with the AI engineer to review agent outputs, identify where knowledge-layer gaps are causing errors, and refine the ontology accordingly Establish and maintain semantic standards and governance frameworks covering versioning, change management and stewardship Document all modelling decisions and change history to support long-term knowledge asset governance Full Technical Skills: Core Semantic Technologies Ontology Languages Query & Validation Reasoning & Logic OWL 2 (DL, EL, RL profiles) RDF/RDFS SKOS SHACL OWL Manchester Syntax Turtle/N-Triples/JSON-LD SPARQL 1.1 SHACL constraint authoring SPARQL reasoning queries Ontology validation tooling Shape expressions (ShEx) Description Logic OWL reasoning (HermiT, Pellet, FaCT ) Inference rule design Formal concept analysis Subsumption reasoning Tooling & Platforms Ontology Editors Triple Stores Version Control Protege TopBraid Composer PoolParty Semaphore VocBench Apache Jena/Fuseki Stardog GraphDB (Ontotext) Amazon Neptune Virtuoso Git-based ontology versioning ROBOT (ontology build tool) Ontology diff tooling CI/CD for ontology pipelines Change log governance Knowledge Architecture Taxonomy & Classification Semantic Modelling AI & Knowledge Systems Taxonomy design and governance Faceted classification Controlled vocabularies Thesaurus construction ISO 25964 standards Domain modelling Concept modelling Entity-relationship design Metadata schema design Linked data principles RAG knowledge layer design Knowledge grounding for LLMs Ontology-driven agent design GraphRAG semantic integration Semantic retrieval patterns Scope and Boundary: This engagement covers group-level operations only. Fund management, investment decision-making and fund-level data are explicitly out of scope. The data environment is manageable in scale and well understood within the delivery team. You will not be working in isolation: the Knowledge Graph Architect, AI engineer and wider team provide context, technical partnership and support. Why Thebes Group: This role offers technically precise, high-impact ontology work on a live AI programme where the semantic layer you build and govern directly determines what agents know and how accurately they perform. You will work within a structured delivery team, reporting into Thebes Group leadership, with clear accountability and real operational stakes.
Role : Ontology Engineer Location: UK Type: Contract Essential Skills: Proven experience in ontology engineering with hands-on OWL, RDF and SKOS delivery in a production or client-facing environment specific semantic ontology and taxonomy experience particularly using tools (Protege and Graphview) Ability to extend and refine existing ontologies, not just build from scratch Strong SPARQL capability including validation and reasoning queries Experience owning and governing taxonomies as versioned, change-controlled assets Understanding of how ontologies and taxonomies feed into AI and RAG systems Strong documentation discipline and ability to record modelling rationale clearly Experience collaborating with technical teams including data engineers and AI developers Highly Desirable: Experience with Protege, TopBraid or equivalent ontology tooling Familiarity with SHACL for constraint validation Background in financial services, private equity or similarly structured enterprise environments Understanding of knowledge grounding principles for large language models Experience with linked data architectures and triple store platforms The Context: Thebes Group is an optimisation company specialising in AI-enabled transformation. We help organisations improve workflow, reporting, information management, and operational decision-making by combining process optimisation, knowledge architecture, semantic technologies, automation, and artificial intelligence. We are currently delivering an AI transformation programme for a private equity group, focused on enhancing group-level operations through intelligent workflows, improved information accessibility, executive reporting, and AI-driven operational intelligence. A foundation ontology and taxonomy already exists. The data is mapped, manageable in scope, and well understood within the team. This is not a build-from-scratch engagement. A Knowledge Graph Architect sits alongside this role to make the semantic structures operational, and an AI engineer handles the agent build. The Ontology Engineer owns the meaning layer: what data is, what the relationships between concepts are, and what the rules are that govern how information should be understood across the organisation. The Role: As Ontology Engineer, you are responsible for the semantic foundation of the programme. You will take the existing ontology and taxonomy and expand, refine and govern them as operational requirements evolve and new agent use cases emerge. Your work defines what the organisation's data means and how concepts relate to each other. That meaning is the input everything else depends on: the knowledge graph, the data pipelines, and ultimately the accuracy of what AI agents know and how they reason. This is a precision role. The quality of your semantic models directly determines the quality of agent outputs across the group. What You Will Do: Expand and maintain the existing ontology as new business requirements emerge, ensuring consistency with the established semantic model Define and refine classes, subclasses, properties, relationships and business rules that accurately represent group-level operational concepts Develop and govern OWL, RDF, RDFS and SKOS artefacts that form the semantic foundation of the programme Own the enterprise taxonomy, managing it as a versioned, governed asset with clear change control and documented rationale Create and maintain SPARQL queries to validate the integrity and consistency of the ontology Work closely with the Knowledge Graph Architect to ensure semantic models translate correctly into graph structures Collaborate with the AI engineer to review agent outputs, identify where knowledge-layer gaps are causing errors, and refine the ontology accordingly Establish and maintain semantic standards and governance frameworks covering versioning, change management and stewardship Document all modelling decisions and change history to support long-term knowledge asset governance Full Technical Skills: Core Semantic Technologies Ontology Languages Query & Validation Reasoning & Logic OWL 2 (DL, EL, RL profiles) RDF/RDFS SKOS SHACL OWL Manchester Syntax Turtle/N-Triples/JSON-LD SPARQL 1.1 SHACL constraint authoring SPARQL reasoning queries Ontology validation tooling Shape expressions (ShEx) Description Logic OWL reasoning (HermiT, Pellet, FaCT ) Inference rule design Formal concept analysis Subsumption reasoning Tooling & Platforms Ontology Editors Triple Stores Version Control Protege TopBraid Composer PoolParty Semaphore VocBench Apache Jena/Fuseki Stardog GraphDB (Ontotext) Amazon Neptune Virtuoso Git-based ontology versioning ROBOT (ontology build tool) Ontology diff tooling CI/CD for ontology pipelines Change log governance Knowledge Architecture Taxonomy & Classification Semantic Modelling AI & Knowledge Systems Taxonomy design and governance Faceted classification Controlled vocabularies Thesaurus construction ISO 25964 standards Domain modelling Concept modelling Entity-relationship design Metadata schema design Linked data principles RAG knowledge layer design Knowledge grounding for LLMs Ontology-driven agent design GraphRAG semantic integration Semantic retrieval patterns Scope and Boundary: This engagement covers group-level operations only. Fund management, investment decision-making and fund-level data are explicitly out of scope. The data environment is manageable in scale and well understood within the delivery team. You will not be working in isolation: the Knowledge Graph Architect, AI engineer and wider team provide context, technical partnership and support. Why Thebes Group: This role offers technically precise, high-impact ontology work on a live AI programme where the semantic layer you build and govern directly determines what agents know and how accurately they perform. You will work within a structured delivery team, reporting into Thebes Group leadership, with clear accountability and real operational stakes.
