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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Head of AI - **Company:** Dyad Artificial Intelligence Limited - **Location:** London, UK - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Application Layers, Computational Linguistics, Data Validation, Graph Database, JSON, Python (Programming Language), Machine Learning, Regression Testing, Data Processing, Large Language Models, Prompt Engineering, Information Technology, Machine Learning Operations, Software Version Control, Automation Anywhere, Elixir - **Published:** July 29, 2026 - **Apply:** https://uk.indeed.com/viewjob?jk=5102a2912605df83 ## About the Role A minimum of a master's degree in computer science with an AI focus or equivalent is required, as well as at least 5+ years commercial experience delivering production AI/NLP systems, with experience operating at architectural or technical leadership levels. 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 background in clinical NLP, entity recognition, and terminology mapping (SNOMED CT, ICD, UMLS). * Experience designing document AI systems using OCR, layout-aware models, or multimodal architectures. Languages & runtime * 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). LLM engineering & LLMOps * 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, even if not a formal ontologist. * 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. ## Description This is a senior, hands-on technical leadership role within the Applied AI function. The Head of AI is responsible for building and leading a team that builds production systems handling unstructured clinical text, structured knowledge (ontologies and graphs), and generative AI. Dyad has a learning and teaching culture and the candidate for the role should be as comfortable coming up with accessible explanations for stakeholders and sharing knowledge with team members as they are digging into technical questions. This role spans a number of disciplines within the ML, NLP, and AI disciplines and is not just another LLM-wrapper position. If your experience is solely around using LLMs within the AI space, this role will not be for you; it is important that a candidate for this role to have broad and integrative understanding of the deep technical foundations of language and machine learning, touching on and including everything from mathematical statistics to computational linguistics, machine learning architectures to system design and evaluation, as well as an understanding of the current state of the art in generative systems. 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 leadership and is critical to scaling our Applied AI delivery. This position is offered on a hybrid basis from our London office. Core responsibilitiesTechnical leadership & architecture ownership * Design and own end-to-end AI architectures that integrate: * + NLP pipelines + LLM-based reasoning and orchestration + Pipeline evaluations and benchmarking + Knowledge graph grounding and validation * Define how structured semantics constrain, validate, and guide generative outputs. * Make pragmatic architectural decisions balancing accuracy, performance, explainability, and engineering effort. * Set standards for system design patterns across the Applied AI stack. * Ensure AI features are production-ready, robust, and aligned with product intent. Day-to-day technical coordination * 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. Evaluation, benchmarking & quality * Define and maintain evaluation frameworks for: * + Hallucination detection + Precision and recall of extracted clinical concepts + Regression testing across model updates * Implement structured output approaches (e.g. schema-constrained generation, ontology-driven formats). * Design iterative feedback loops, including human-in-the-loop review where appropriate. * Ensure measurable improvements in grounding, explainability, and reliability over time. Compliance-aware AI engineering * 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. ## Related Videos - [Three years of putting LLMs into Software - Lessons learned](https://www.wearedevelopers.com/videos/1508-three-years-of-putting-llms-into-software-lessons-learned) - [Putting the Graph In GraphQL With The Neo4j GraphQL Library](https://www.wearedevelopers.com/videos/257-putting-the-graph-in-graphql-with-the-neo4j-graphql-library) - [Bringing digital education to refugee and host communities in remote regions of Africa](https://www.wearedevelopers.com/videos/644-bringing-digital-education-to-refugee-and-host-communities-in-remote-regions-of-africa) - [Tips and Tricks for Working with JSON](https://www.wearedevelopers.com/videos/1229-tips-and-tricks-for-working-with-json) - [Give Your LLMs a Left Brain](https://www.wearedevelopers.com/videos/1160-give-your-llms-a-left-brain) - [The Limits of LLMs in Real-World Applications](https://www.wearedevelopers.com/videos/100328-the-limits-of-llms-in-real-world-applications) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)