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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 about best practices in building ontologies that support AI applications.
✨Tip Number 2
Gain hands-on experience with both Semantic Graph and Property Graph technologies. Consider working on personal projects or contributing to open-source initiatives that utilise tools like Neo4j or Amazon Neptune, as this practical knowledge will set you apart during interviews.
✨Tip Number 3
Network with professionals in the field of network science and graph databases. Attend webinars, workshops, or local meetups to build connections and learn from others who have successfully navigated similar roles.
✨Tip Number 4
Prepare to discuss your mentoring experiences and how you've fostered a culture of learning in previous roles. Highlight specific examples where you've trained junior team members, as this aligns well with the responsibilities of the Ontology Consultant position.
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 and graph database technologies. Include specific examples of projects where you've designed or implemented ontologies, and mention any relevant tools like Neo4j or Amazon Neptune.
Craft a Compelling Cover Letter: In your cover letter, express your passion for ontology development and knowledge engineering. Discuss how your skills align with the responsibilities outlined in the job description, particularly your experience with AI integration and network science.
Showcase Relevant Projects: If you have worked on projects involving semantic reasoning or network analytics, be sure to include these in your application. Describe your role and the impact of your contributions, especially in relation to AI systems and data pipelines.
Highlight Mentorship Experience: Since the role involves mentoring junior team members, mention any previous experience you have in training or guiding others. This will demonstrate your leadership skills and commitment to fostering a collaborative work environment.
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, especially in relation to data integration and semantic reasoning.
✨Demonstrate Graph Database Knowledge
Familiarise yourself with both Semantic Graph and Property Graph technologies. Be ready to explain how you've used tools like Neo4j or Amazon Neptune in past roles, and how they can be integrated with AI systems.
✨Discuss Network Science Applications
Prepare to talk about how you've applied network science techniques in your work. Share examples of algorithms or models you've developed to extract insights from graph structures, and how these insights can enhance AI capabilities.
✨Emphasise Mentoring Experience
If you have experience mentoring junior team members, make sure to mention it. Discuss how you've fostered a culture of learning and growth within your teams, as this is a valuable trait for the role.
