At a Glance
- Tasks: Expand and govern semantic knowledge structures for AI agents in a dynamic environment.
- Company: Join Thebes Group, a leading optimisation company driving AI transformation.
- Benefits: Competitive contract rate, collaborative team, and opportunity to shape AI operations.
- Other info: Supportive onboarding with a focus on career growth and innovation.
- Why this job: Make a real impact on AI-driven decision-making and operational efficiency.
- Qualifications: Experience in ontology, taxonomy, and knowledge graph management required.
The predicted salary is between 60000 - 80000 £ per year.
Location: London
Duration: Contract
The Context: Thebes Group is an Optimisation Company. We are engaged on an AI transformation programme for a private equity group, focused exclusively on group-level operations, not fund management or investment activity. The programme is building AI agents to support day-to-day group operations: workflow, reporting, information management and operational decision-making. A foundation ontology and taxonomy already exists, and the organisation's data is mapped and manageable in scope. This is not a greenfield engagement. The Semantic Knowledge Architect joins an established delivery team that includes an AI engineer responsible for agent development. The two roles work in close partnership: the AI engineer builds and maintains the agents; this role owns the knowledge layer those agents depend on.
The Role: Your responsibility is the quality, integrity and evolution of the semantic knowledge layer: the ontologies, taxonomies and knowledge graph structures that determine what agents know, how they connect information, and whether their outputs are accurate. You will expand and govern structures that are already in place, working iteratively as the programme develops and agent use cases grow. You will also be the diagnostic layer between agent outputs and the knowledge layer: when an agent produces an incorrect or incomplete output, you identify whether the root cause sits in the knowledge structure and fix it at source. This is a technically precise, high-accountability role. The accuracy of agent outputs across the group depends directly on the quality of the knowledge layer you maintain.
How You Will Work: The role follows a continuous iterative cycle across four activities:
- Expand: take new or evolving business and technical requirements and extend the existing ontology and taxonomy to accommodate them, maintaining consistency with the established model
- Govern: manage the ontology and taxonomy as controlled, versioned assets with documented change rationale, ownership and review cycles
- Validate: review agent outputs in collaboration with the AI engineer, identify gaps or inaccuracies that originate in the knowledge layer, and trace them to their structural source
- Iterate: update ontologies, taxonomies and graph structures based on validation findings, closing the loop between agent performance and knowledge quality
You will work closely with other team members for data context and domain knowledge. The data landscape is already understood within the team, so onboarding to the knowledge environment will be well supported.
What You Will Do:
- Extend the existing ontology to reflect new business requirements, additional entities and evolving operational concepts
- Expand and maintain the enterprise taxonomy, ensuring classification remains accurate, consistent and fit for agent consumption
- Own the governance framework for both the ontology and taxonomy: versioning, change control, documentation and review cadence
- Work with the AI engineer to review agent outputs and identify where knowledge-layer gaps or inconsistencies are driving errors
- Update ontological and taxonomic structures in response to validated agent performance issues
- Maintain the knowledge graph as an accurate, traversable semantic layer connecting group operational data
- Ensure data ingestion into systems is governed by clear metadata and semantic standards
- Document all structural decisions, changes and rationale to support long-term knowledge asset governance
- Contribute to the broader delivery team, sharing knowledge context with data, platform and business colleagues as needed
Technical Pillars: The role operates across four connected disciplines that together form the full knowledge governance and agent quality chain:
- Ontology Expansion
Knowledge Engineer employer: Thebes Group
Thebes Group is an exceptional employer, offering a dynamic work environment in London where innovation meets collaboration. As a Knowledge Engineer, you will be part of a dedicated team driving AI transformation, with ample opportunities for professional growth and development. The company fosters a culture of continuous improvement and accountability, ensuring that your contributions directly impact the quality of AI outputs and operational efficiency.
StudySmarter Expert Advice🤫
We think this is how you could land Knowledge Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with professionals on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Prepare for interviews by practising common questions related to knowledge engineering and AI. We recommend doing mock interviews with friends or using online platforms to get comfortable with your responses.
✨Tip Number 3
Showcase your skills through projects or a portfolio. If you've worked on relevant projects, make sure to highlight them during interviews. This gives you a chance to demonstrate your expertise in ontology and taxonomy management.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Knowledge Engineer
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter for the Knowledge Engineer role. Highlight your experience with ontologies, taxonomies, and knowledge graphs, as these are key to what we’re looking for. Show us how your skills align with our needs!
Showcase Your Technical Skills:Don’t shy away from detailing your technical expertise in your application. We want to see your familiarity with semantic structures and how you’ve tackled similar challenges in the past. Be specific about the tools and methodologies you've used!
Be Clear and Concise:When writing your application, clarity is crucial. Use straightforward language and avoid jargon unless it’s relevant. We appreciate a well-structured application that gets straight to the point while still showcasing your personality.
Apply Through Our Website:We encourage you to submit your application through our website. It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it makes tracking your application easier for both of us!
How to prepare for a job interview at Thebes Group
✨Know Your Ontologies
Make sure you brush up on your understanding of ontologies and taxonomies before the interview. Be ready to discuss how you've previously expanded or governed these structures, as this role heavily relies on your ability to manage knowledge layers effectively.
✨Collaborate Like a Pro
Since you'll be working closely with an AI engineer, think about examples where you've successfully collaborated in a team setting. Highlight your communication skills and how you’ve contributed to joint problem-solving, especially when it comes to diagnosing issues in knowledge structures.
✨Showcase Your Iterative Mindset
This role involves continuous iteration, so come prepared to discuss how you've approached iterative processes in past projects. Share specific instances where you've updated or refined knowledge structures based on feedback or performance issues.
✨Document Everything
Emphasise the importance of documentation in your previous roles. Be ready to explain how you've maintained clear records of changes and decisions in knowledge governance, as this will be crucial for the accountability aspect of the position.
