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- Tasks: Design and maintain ontologies for data integration and AI applications.
- Company: Join a forward-thinking company at the forefront of AI and knowledge engineering.
- Benefits: Enjoy remote work flexibility and opportunities for professional growth.
- Why this job: Be part of an innovative team shaping the future of AI with impactful projects.
- Qualifications: Strong background in ontologies, graph databases, and network science required.
- Other info: Initial 6-month contract with potential for extension.
The predicted salary is between 48000 - 72000 £ per year.
Role = Ontology Consultant
Duration = Initial 6-month contract
Location = UK (Remote)
Job Spec
Key Responsibilities
- Ontology Development and Knowledge Engineering
- Design, build, and maintain ontologies to support data integration and semantic reasoning.
- Leverage ontologies to enhance data pipelines and enable advanced knowledge engineering solutions.
- Collaborate with AI teams to ensure ontology structures efficiently support Agentic AI applications, including RAG pipelines and Agent Orchestration .
- Graph Database Expertise
- Work with both Semantic Graph and Property Graph technologies, understanding their unique architectures and use cases.
- Utilize Semantic Graph tools for rule-based inference and semantic reasoning.
- Employ Property Graph tools like Neo4j , Amazon Neptune , and TigerGraph for network analytics and data exploration.
- Integrate graph solutions with AI and machine learning systems, ensuring seamless knowledge retrieval and reasoning.
- Network Science Application
- Apply network science techniques to analyze and interpret complex relationships within graph data.
- Develop algorithms and models to extract insights from graph structures and relationships.
- Use network insights to enhance AI systems’ ability to reason across interconnected data sets.
Skills
Required Skills and Qualifications
- Strong expertise in ontologies , including their design, implementation, and application in real-world scenarios.
- Proficiency in graph database technologies , with hands-on experience in both Semantic Graph and Property Graph systems.
- Solid understanding of network science concepts and their practical applications in graph engineering.
- Familiarity with standards such as RDF and W3C for Semantic Graphs, as well as bespoke standards for Property Graphs.
- Ability to mentor and train junior team members, fostering a culture of learning and growth.
- Experience integrating AI considerations (like LLM-based retrieval, RAG pipelines, and Agent Orchestration) into graph ecosystem design.
Preferred Qualifications
- Experience with data pipelines and integrating graph databases into larger data ecosystems.
- Knowledge of rule-based inference systems and network analytics tools.
- Strong problem-solving skills and the ability to work independently on complex technical challenges.
- Prior exposure to enterprise AI initiatives , demonstrating an understanding of how knowledge graphs support agent-based architectures.
Ontology Consultant employer: Russell Tobin
Contact Detail:
Russell Tobin Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Ontology Consultant
✨Tip Number 1
Familiarise yourself with the latest trends in ontology development and knowledge engineering. Join online forums or communities where professionals discuss their experiences and share insights, as this can help you understand what employers like us are looking for.
✨Tip Number 2
Showcase your hands-on experience with graph database technologies by working on personal projects or contributing to open-source initiatives. This practical experience can set you apart and demonstrate your ability to apply your skills in real-world scenarios.
✨Tip Number 3
Network with professionals in the field of ontology consulting and graph databases. Attend relevant webinars, workshops, or conferences to make connections and learn about potential job openings directly from industry insiders.
✨Tip Number 4
Prepare to discuss how you've integrated AI considerations into your previous projects. Being able to articulate your experience with LLM-based retrieval and RAG pipelines will show us that you understand the intersection of AI and graph technologies.
We think you need these skills to ace Ontology Consultant
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your expertise in ontologies, graph databases, and network science. Use specific examples from your past experiences that align with the key responsibilities mentioned in the job description.
Craft a Compelling Cover Letter: In your cover letter, express your passion for ontology development and knowledge engineering. Mention how your skills can contribute to the company's AI initiatives and provide examples of your previous work with Semantic and Property Graph technologies.
Showcase Relevant Projects: If you have worked on projects involving graph databases or network science, include them in your application. Describe your role, the challenges faced, and the outcomes achieved to demonstrate your practical experience.
Highlight Mentorship Experience: Since the role requires mentoring junior team members, mention any relevant experience you have in training or guiding others. This will show your ability to foster a culture of learning and growth within the team.
How to prepare for a job interview at Russell Tobin
✨Showcase Your Ontology Expertise
Be prepared to discuss your experience with ontology design and implementation. Highlight specific projects where you've successfully built or maintained ontologies, and how they supported data integration and semantic reasoning.
✨Demonstrate Graph Database Knowledge
Familiarise yourself with both Semantic Graph and Property Graph technologies. Be ready to explain their architectures and use cases, and share examples of how you've used tools like Neo4j or Amazon Neptune in past projects.
✨Discuss Network Science Applications
Prepare to talk about how you've applied network science techniques in your work. Discuss any algorithms or models you've developed to extract insights from graph structures, and how these insights enhanced AI systems.
✨Emphasise Mentorship and Collaboration
Since mentoring junior team members is part of the role, think of examples where you've trained or guided others. Highlight your collaborative experiences, especially with AI teams, to show your ability to foster a culture of learning.
