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 Responsibilities

Technical 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.

Requirements

• 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.

Benefits

• Competitive Salary
• Company Pension
• 25 days of paid annual leave (pro-rata)
• A fun and flexible hybrid working environment
• Access to our Employee Assistance Programme - Health Assured
• A modern, dog-friendly office located near Chancery Lane with free drinks