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- Tasks: Lead the development of knowledge graphs using cutting-edge semantic technologies.
- Company: Join a leading Regulatory Tech company with a focus on innovation.
- Benefits: Competitive daily rate, remote work flexibility, and potential for contract extension.
- Why this job: Make an impact in the tech world while working on exciting projects until 2026.
- Qualifications: Experience with W3C standards, graph databases, and Python ML/NLP tools required.
- Other info: Opportunity to work remotely with occasional site visits and excellent career growth.
The predicted salary is between 48000 - 72000 £ per year.
A leading Regulatory Tech company is seeking a Contract Lead Knowledge Graph Engineer/Ontology Engineer, to support a range of initiatives throughout 2026.
Responsibilities
- 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.
- 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.
Lead Knowledge Graph Engineer/Ontology Engineer - Contract in Manchester employer: Involved Solutions
Contact Detail:
Involved Solutions Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead Knowledge Graph Engineer/Ontology Engineer - Contract in Manchester
✨Tip Number 1
Network like a pro! Reach out to your connections in the industry and let them know you're on the lookout for opportunities. You never know who might have a lead or can put in a good word for you.
✨Tip Number 2
Get your online presence sorted! Make sure your LinkedIn profile is up-to-date and showcases your skills in RDF, SPARQL, and Python. Join relevant groups and engage with posts to get noticed by potential employers.
✨Tip Number 3
Prepare for interviews by brushing up on your technical skills. Be ready to discuss your experience with graph databases and NLP techniques. Practise explaining complex concepts in simple terms—this will impress interviewers!
✨Tip Number 4
Don't forget to apply through our website! We love seeing candidates who are proactive. Plus, it gives you a better chance of being noticed by our hiring team. So, get those applications in!
We think you need these skills to ace Lead Knowledge Graph Engineer/Ontology Engineer - Contract in Manchester
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the role of Lead Knowledge Graph Engineer. Highlight your experience with W3C standards, graph databases, and any relevant NLP techniques. We want to see how your skills match what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about this role and how your background makes you the perfect fit. Don’t forget to mention any specific projects or experiences that relate to the responsibilities listed.
Showcase Your Technical Skills: In your application, be sure to showcase your technical skills clearly. Mention your familiarity with tools like RDF, SPARQL, and Python libraries. We love seeing concrete examples of how you've used these in past projects!
Apply Through Our Website: We encourage you to apply through our website for a smoother process. It helps us keep track of applications and ensures you get all the updates directly from us. Plus, it’s super easy!
How to prepare for a job interview at Involved Solutions
✨Know Your Tech Inside Out
Make sure you’re well-versed in the core semantic and graph technologies mentioned in the job description. Brush up on W3C standards like RDF, RDFS, and SPARQL, as well as your experience with graph databases. Being able to discuss these confidently will show that you’re the right fit for the role.
✨Showcase Your NLP/NLU Skills
Prepare to talk about your experience with modern NLP/NLU techniques. Be ready to share specific examples of how you've applied topic modelling, entity extraction, or text summarisation in past projects. This will demonstrate your practical knowledge and ability to contribute immediately.
✨Visualisation is Key
Since the role involves translating complex concepts to varied audiences, practice explaining your work with graph visualisation tools like Linkurious or GraphViz. Think of a few scenarios where you’ve successfully communicated intricate ideas to non-technical stakeholders.
✨Prepare for Problem-Solving Questions
Expect questions that assess your problem-solving skills, especially related to data pipelines and entity resolution techniques. Prepare to discuss challenges you've faced in previous roles and how you overcame them, particularly in relation to data cleansing and integration.
