At a Glance
- Tasks: Design and operationalise AI architectures integrating generative AI and knowledge graphs.
- Company: Join Dyad, a forward-thinking tech company in the healthcare sector.
- Benefits: Enjoy flexible working, 25 days leave, and a modern office environment.
- Other info: Work in a dynamic startup with high ownership and growth potential.
- Why this job: Lead innovative AI projects that make a real difference in healthcare.
- Qualifications: Master's degree in computer science and 3+ years in AI systems required.
The predicted salary is between 80000 - 100000 £ per year.
Dyad is seeking a Semantic Architect to design and operationalise our graph-integrated generative AI architecture. This is a senior, hands-on technical leadership role within the Applied AI function. The Semantic Architect is responsible for ensuring that unstructured clinical text, structured knowledge (ontologies and graphs), and generative AI systems work together as a coherent, production-ready system. You will bridge NLP pipelines, LLM-based reasoning, and knowledge graph grounding to produce outputs that are accurate, explainable, and suitable for use in regulated healthcare environments. The role combines architectural ownership with day-to-day technical coordination and is critical to scaling Applied AI delivery without creating single points of technical dependency. This position is offered on a hybrid basis from our London office.
Core responsibilities
- Technical leadership & architecture ownership
- Design and own end-to-end AI architectures that integrate:
- LLM-based reasoning and orchestration
- Pipeline evaluations and benchmarking
- Knowledge graph grounding and validation
- Coordinate technical work within the Applied AI team.
- Break product requirements into coherent, technically sound implementation plans.
- Ensure alignment between NLP components, graph systems, and application layers.
- Maintain architectural coherence as features evolve and scale.
- Represent Applied AI in cross-functional technical discussions with Engineering and Product.
- Define and maintain evaluation frameworks for:
- Precision and recall of extracted clinical concepts
- Regression testing across model updates
- Design AI workflows that embed traceability, auditability, and data minimisation by default.
- Ensure architectural decisions align with medical device and data protection requirements across UK and US contexts.
- Work proactively with Clinical Safety and QARA teams to avoid late-stage architectural risk.
Requirements
- A minimum of a master's degree in computer science with an AI focus or equivalent is required, as well as at least 3+ years commercial experience delivering knowledge-based systems in real production environments.
Core technical expertise
- Strong hands-on experience in designing production AI systems that integrate LLMs with structured knowledge.
- Deep understanding of trade-offs between symbolic reasoning, probabilistic inference, and generative pattern matching.
- Experience building systems that combine NLP pipelines with structured data validation or knowledge graphs.
- Strong Python experience for NLP pipelines, LLM orchestration, evaluation tooling, and data processing.
- Experience integrating AI systems into production services (Elixir experience is a plus, or willingness to engage deeply with it).
- Experience with prompt engineering using structured outputs.
- Familiarity with schema-constrained generation (e.g. JSON or ontology-driven outputs).
- Experience designing evaluation and benchmarking frameworks for production LLM systems.
- Understanding of model versioning, regression testing, and iterative improvement cycles.
Knowledge graph integration
- Experience designing AI pipelines that are constrained or validated by graph structures.
- Ability to collaborate effectively with Knowledge Engineers to ensure graph representations are AI-usable.
- Understanding of performance and scaling considerations when integrating graph-backed validation.
Operating context
- Experience working in regulated or high-assurance environments is strongly preferred.
- Ability to balance experimentation with production discipline.
- Comfortable operating in a fast-moving startup environment with high ownership expectations.
Personal attributes
- Systems-oriented thinker who values coherence over novelty.
- Pragmatic builder rather than research-focused experimentalist.
- Comfortable taking technical ownership and accountability.
- Strong communicator who documents and disseminates architectural knowledge to avoid bottlenecks.
Benefits
- Company pension
- 25 days of paid annual leave (pro-rata)
- Flexible hybrid working environment
- Employee Assistance Programme
- Modern, dog-friendly office near Chancery Lane with free drinks
Semantic Architect in London employer: Dyad AI
Contact Detail:
Dyad AI Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Semantic Architect in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to AI architectures and NLP pipelines. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Be ready to discuss your experience with LLMs, knowledge graphs, and how you've tackled challenges in past projects.
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you're genuinely interested in joining our team at Dyad.
We think you need these skills to ace Semantic Architect in London
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter to highlight your experience with AI architectures and knowledge graphs. We want to see how your skills align with the role of Semantic Architect, so don’t hold back on showcasing relevant projects!
Showcase Your Technical Skills: When detailing your experience, focus on your hands-on work with LLMs, NLP pipelines, and structured data validation. We’re looking for someone who can bridge these areas effectively, so be specific about your contributions and outcomes.
Be Clear and Concise: Keep your application straightforward and to the point. Use clear language to describe your achievements and avoid jargon unless it’s relevant to the role. We appreciate clarity as much as technical prowess!
Apply Through Our Website: Don’t forget 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 shows you’re keen on joining our team at StudySmarter!
How to prepare for a job interview at Dyad AI
✨Know Your Stuff
Make sure you brush up on your knowledge of LLMs, NLP pipelines, and knowledge graphs. Be ready to discuss how you've integrated these technologies in past projects, as well as the trade-offs you've navigated between accuracy and performance.
✨Showcase Your Leadership Skills
As a Semantic Architect, you'll need to demonstrate your technical leadership. Prepare examples of how you've coordinated technical work within teams, broken down complex requirements into actionable plans, and maintained architectural coherence during feature evolution.
✨Understand Compliance Requirements
Familiarise yourself with the compliance aspects of AI engineering, especially in regulated environments. Be prepared to discuss how you've ensured traceability and auditability in your previous projects, and how you plan to align architectural decisions with medical device regulations.
✨Communicate Clearly
Strong communication is key in this role. Practice explaining complex concepts in simple terms, and be ready to share how you've documented architectural knowledge to avoid bottlenecks in your previous roles. This will show that you're not just a tech whiz, but also a team player.