Lead Knowledge Graph Engineer

Involved Solutions; Remote; GBP 80,000 - 100,000; Full time

Job summary

A leading Regulatory Tech company is looking for a Contract Lead Knowledge Graph Engineer/Ontology Engineer to contribute through various projects until 2026. The role involves applying W3C semantic standards, modern NLP techniques, and delivering quality production code in Python. Candidates must demonstrate proficiency in graph databases and communication skills to relay complex concepts effectively. The position offers a competitive rate of £600 per day and is primarily remote with occasional travel required.

Qualifications

• Experience with RDF, RDFS, OWL, and SPARQL.
• Ability to work with entity resolution and data pipelines.
• Familiarity with graph-based machine learning techniques.

Responsibilities

• Harness W3C semantic standards and tooling for modeling.
• Apply modern NLP/NLU techniques in projects.
• Translate complex graph or AI concepts to varied audiences.

Skills

Knowledge of W3C semantic standards

Graph databases

Python programming

NLP/NLU techniques

Tools

GraphDB

TensorFlow

Neo4j

Job description

Overview

Lead Knowledge Graph Engineer/Ontology Engineer - Contract

Duration: 6 months (extendable)

Rate: £600 per day

IR35: Outside

Location: Remote with occasional travel to site

The Role: A leading Regulatory Tech company is seeking a Contract Lead Knowledge Graph Engineer/Ontology Engineer, to support a range of initiatives throughout 2026.

Responsibilities

In this role, you will have the opportunity to:

• Harness W3C semantic standards and tooling—RDF/RDFS, SPARQL, OWL, SHACL—together with graph databases, ontology-design tools, and visualization platforms such as Linkurious.
• Apply modern NLP/NLU techniques, from topic modelling to cutting edge entity and relation extraction, plus concise text summarisation.

Experience Requirements

Core semantic/graph tech

• W3C standards and tooling: RDF, RDFS, SKOS, OWL, SHACL, SPARQL for modelling, validation and querying.
• Graph databases and platforms: GraphDB, Stardog, Amazon Neptune, Neo4j, TigerGraph, ArangoDB or similar RDF/LPG stores.
• Ontology and knowledge graph frameworks, reasoning tools, and production deployment experience.

Data pipelines and entity work

• ETL/streaming or CDC pipelines feeding a knowledge graph.
• Entity resolution techniques, data cleansing, enrichment, and integration from many sources.

Python + ML/NLP stack

• High quality production code in Python.
• Libraries such as NetworkX, TensorFlow or PyTorch, NLTK, spaCy, Hugging Face, BERT, Pandas, NumPy, scikit learn.
• Graph based ML familiarity: link prediction, anomaly detection, traversal, community detection.
• NLP/NLU skills: entity/relation recognition, summarisation, topic modelling, classification, coreference resolution.

Visualization and communication

• Graph visualisation tools: Linkurious, Ogma, GraphViz, PyVis, PyDot, etc.
• Ability to translate complex graph or AI concepts to varied audiences.

If you are available and interested, please apply in the first instance and you will be contacted to discuss the position further.