At a Glance
- Tasks: Design and manage a cutting-edge knowledge graph platform for AI transformation.
- Company: Join Thebes Group, a leader in AI-enabled optimisation.
- Benefits: Competitive contract rate, flexible working, and hands-on experience with innovative technologies.
- Other info: Collaborate with a dynamic team and grow your skills in a fast-paced environment.
- Why this job: Make a real impact on AI projects that enhance operational intelligence.
- Qualifications: Experience with Neo4j, ETL pipelines, and knowledge graph architecture.
The predicted salary is between 60000 - 80000 £ per year.
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, taxonomy and initial knowledge graph exist. The data is mapped, manageable in scope and well understood within the team. An Ontology Engineer sits alongside this role to own the semantic foundation and an AI engineer handles the agent build.
The Knowledge Graph Architect takes the semantic models produced by the Ontology Engineer and makes them operational: designing and running the graph platform, pipelines and integration layer that AI agents query and depend on.
The Role: As Knowledge Graph Architect, you are responsible for the operational knowledge layer: the graph database, data pipelines, integration architecture and platform governance that sit between the semantic models and the AI agents consuming them. You will expand and maintain the existing knowledge graph as the programme evolves, ensure data flows correctly from source systems into the graph and work closely with the AI engineer to make the knowledge layer accessible, performant and reliable for agent use. Where agent outputs are incorrect, you will work with the Ontology Engineer and AI engineer to identify whether the problem sits in graph structure, data integration or platform performance, and resolve it at source.
Responsibilities
- Design and extend the enterprise knowledge graph architecture as operational requirements grow
- Implement and maintain graph database infrastructure using Neo4j or equivalent platforms
- Build and manage ETL/ELT pipelines that ingest group operational data into the knowledge graph accurately and consistently
- Design and optimise Cypher queries for agent consumption, analytics and operational reporting
- Integrate the knowledge graph with LLM and RAG architectures to support AI agent knowledge retrieval
- Implement GraphRAG patterns to enable agents to traverse and reason over graph-structured knowledge
- Ensure graph platform governance including security, access control, versioning and operational monitoring
- Work closely with the Ontology Engineer to ensure graph structures accurately reflect the semantic model
- Collaborate with the AI engineer to optimise how agents query and consume the knowledge graph
- Identify and resolve data integration issues that cause agent output failures or knowledge retrieval errors
Full Technical Skills
- Neo4j (Enterprise)
- Amazon Neptune
- Stardog
- GraphDB (Ontotext)
- TigerGraph
- Azure Cosmos DB (Gremlin API)
- Cypher
- SPARQL 1.1
- Gremlin
- GQL (ISO standard)
- openCypher
- SPARQL Update
- Property graph modelling
- RDF graph modelling
- Labelled property graphs
- Hypergraph structures
- Entity resolution
- Graph schema design
- Data Engineering & Integration Pipeline Development
- ETL/ELT pipeline design
- Apache Kafka
- Apache Spark
- AWS Glue
- dbt
- Apache Airflow
- Entity matching and resolution
- Data lineage tracking
- Schema mapping
- Semantic data integration
- Metadata management
- Data quality frameworks
- Python (networkx, rdflib, py2neo)
- SQL
- Bash/Shell Scripting
- REST API integration
- GraphQL
- JSON-LD processing
- AI & Cloud Architecture
- RAG architecture design
- GraphRAG implementation
- Vector database integration
- LLM knowledge grounding
- Semantic retrieval design
- Agent knowledge API design
- AWS (Neptune, Glue, S3, Lambda)
- Azure (Cosmos DB, Synapse)
- GCP (Vertex AI, BigQuery)
- Terraform/IaC
- Docker/Kubernetes
- CI/CD pipeline management
- Graph performance optimisation
- Access control and security
- Backup and recovery
- Monitoring and alerting
- Schema versioning
- Operational runbooks
Essential
- Proven experience designing and implementing knowledge graphs in a production or client-facing environment
- Hands‑on Neo4j or equivalent graph database capability including Cypher query design and optimisation
- Experience building ETL/ELT pipelines that feed structured data into graph platforms
- Understanding of how knowledge graphs integrate with RAG and LLM architectures
- Ability to work from an ontologist's semantic model and implement it faithfully in graph structures
- Strong Python capability for data pipeline and graph integration work
- Experience in regulated or enterprise environments where data accuracy and platform reliability are critical
Highly Desirable
- Experience with GraphRAG patterns and graph-based semantic retrieval for AI agents
- Familiarity with SPARQL and RDF-based graph platforms alongside property graph experience
- AWS cloud architecture experience, particularly Neptune, Glue and Lambda
- Background in financial services, private equity or similarly structured enterprise environments
- Experience with vector database integration alongside knowledge graph platforms
- Knowledge of graph governance, schema versioning and operational monitoring at scale
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 already understood within the delivery team. You will work in close partnership with the Ontology Engineer and AI engineer, with clear role boundaries and shared accountability for the quality of what the agents produce.
Why Thebes Group
This role offers the opportunity to build and operate a knowledge graph platform that sits at the heart of a live AI transformation programme. The graph you design and run is the operational layer that agents depend on. Its accuracy, performance and integrity directly determine the quality of what the programme delivers. You will work within a structured delivery team, reporting into Thebes Group leadership, with clear accountability and real operational stakes.
Knowledge Graph Architect in City of Westminster employer: Thebes IT Solutions Ltd
Thebes Group is an exceptional employer, offering a dynamic work environment where innovation meets collaboration. As a Knowledge Graph Architect, you will play a pivotal role in a cutting-edge AI transformation programme, with ample opportunities for professional growth and development. Our supportive culture fosters teamwork and accountability, ensuring that your contributions directly impact the success of our projects while enjoying the benefits of working in the vibrant UK tech landscape.
StudySmarter Expert Advice🤫
We think this is how you could land Knowledge Graph Architect in City of Westminster
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We think you need these skills to ace Knowledge Graph Architect in City of Westminster
Some tips for your application 🫡
Showcase Your Projects:When applying for a freelance data science role like Knowledge Graph Architect at Thebes IT Solutions Ltd, it’s crucial to highlight your projects. Include a portfolio that features at least two or three projects involving data analysis, machine learning, or visualisation. Make sure to describe the tools and methodologies you used, so we can see your skills in action!
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How to prepare for a job interview at Thebes IT Solutions Ltd
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