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For Companies At a Glance
- Tasks: Design and maintain ontologies for data integration and AI applications.
- Company: Join a forward-thinking company focused on innovative AI solutions.
- Benefits: Enjoy remote work flexibility and opportunities for professional growth.
- Why this job: Be part of cutting-edge projects that shape the future of AI and knowledge engineering.
- Qualifications: Strong background in ontologies and graph databases is essential.
- Other info: This is a 6-month contract role with potential for extension.
The predicted salary is between 48000 - 72000 £ per year.
Job Description
Role: Ontology Consultant
Duration: Initial 6-month contract
Location: UK (Remote)
Duties
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 Specialist employer: eTeam Inc
Contact Detail:
eTeam Inc Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Ontology Specialist
✨Tip Number 1
Familiarise yourself with the latest trends in ontology development and knowledge engineering. This will not only help you understand the role better but also allow you to engage in meaningful conversations during interviews.
✨Tip Number 2
Network with professionals in the field of graph databases and ontology design. Attend relevant webinars or join online forums where you can discuss your insights and learn from others' experiences.
✨Tip Number 3
Showcase your hands-on experience with Semantic Graph and Property Graph technologies by working on personal projects or contributing to open-source initiatives. This practical experience can set you apart from other candidates.
✨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 demonstrate your relevance to the role.
We think you need these skills to ace Ontology Specialist
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 you've used.
Craft a Compelling Cover Letter: In your cover letter, explain why you're passionate about ontology development and how your skills align with the role. Mention your experience with AI integrations and network science, and how you can contribute to the company's goals.
Showcase Relevant Projects: If you have worked on projects involving Semantic Graphs or Property Graphs, be sure to include these in your application. Describe your role, the challenges faced, and the outcomes achieved to demonstrate your hands-on experience.
Highlight Mentorship Experience: Since the role requires mentoring junior team members, include any relevant experience you have in training or guiding others. This shows your leadership potential and commitment to fostering a collaborative environment.
How to prepare for a job interview at eTeam Inc
✨Showcase Your Ontology Expertise
Be prepared to discuss your experience with ontology design and implementation. Highlight specific projects where you've successfully built ontologies that support data integration and semantic reasoning, as this will demonstrate your practical knowledge in real-world scenarios.
✨Demonstrate Graph Database Proficiency
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 projects, and discuss the unique architectures and use cases of these systems to show your depth of understanding.
✨Discuss Network Science Applications
Prepare to talk about how you've applied network science techniques to analyse complex relationships within graph data. Share examples of algorithms or models you've developed to extract insights, as this will illustrate your ability to enhance AI systems through interconnected data sets.
✨Emphasise Mentorship and Collaboration
Since the role involves mentoring junior team members, be ready to share your experiences in training others. Discuss how you foster a culture of learning and collaboration, which is crucial for building a strong team dynamic in any technical environment.
