Head of AI | Natural Language Processing | Knowledge Graphs | Semantic Web | Large Language Mod[...]

Enigma

Greater London

Hybrid

GBP 140,000 - 190,000

Full time

2 days ago

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Benefits offered by this job

Company pension

25 days paid annual leave (pro-rata)

Hybrid working environment

Employee Assistance Programme

Dog-friendly office near Chancery Lane

Job summary

Enigma, a London-based startup, is seeking a seasoned Head of AI to lead a team building a graph-integrated generative AI platform for healthcare. The role focuses on bridging NLP pipelines, LLM-based reasoning, and knowledge graph grounding to deliver explainable, production-ready AI solutions in regulated environments.

The position combines architectural ownership with hands-on leadership, requiring deep expertise across machine learning, statistics, NLP, and system design, with a preference

Qualifications

Master's degree in CS with AI focus or equivalent.
5+ years of commercial AI/NLP production experience.
Experience at architectural/technical leadership level.
Strong Python for NLP pipelines and ML tooling.

Responsibilities

Design end-to-end AI architectures integrating LLMs with structured data.
Coordinate Applied AI team and define implementation plans.
Ensure production readiness with reliability and explainability.
Align NLP components with graph systems and products.
Develop evaluation frameworks for concept extraction accuracy.

Skills

AI architecture

NLP pipelines

Knowledge graphs

Regulatory compliance

Leadership

Education

Master's degree in CS with AI focus

Tools

Python

Elixir

Job description

Company Overview

Our mission is to improve the delivery and efficiency of healthcare.

We are building a platform to model and manage the flow of information within healthcare organisations, improving outcomes for patients, payers, and healthcare providers. We believe data handling in current healthcare systems is needlessly complex and disconnected, leading to isolated and inefficient decision making. To showcase how this technology can advance the delivery of healthcare and improve lives, we build and deploy products for healthcare providers and payers across the UK and US markets.

We are an energetic, early-stage startup of around twenty people. Our team is growing as we explore new markets and opportunities. We are passionate about technology and its application to meaningful, real-world problems. New joiners have a significant impact on both the direction of the company and its culture.

Our products

Our products are founded on a Semantic AI platform, combining knowledge graphs and generative AI to deliver grounded, explainable intelligence for healthcare workflows.

Primary Care Operations

We develop a suite of products supporting healthcare operations, including AI-powered tools that help GP practices reduce administrative burden when processing clinical correspondence. These tools support clinical coding, follow-up task identification, and workflow optimisation, helping practices save time and cost, improve audit performance, and build operational resilience.

We are seeking a Head of AI to lead a team that designs and operationalises our graph-integrated generative AI architecture.

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 develops production systems handling unstructured clinical text, structured knowledge (ontologies and graphs), and generative AI.

We have 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 ML, NLP, and AI 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 has a broad and integrative understanding of the deep technical foundations of language and machine learning, touching on everything from mathematical statistics and computational linguistics to machine learning architectures, 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. Candidates must be in the UK or planning an immediate relocation for their application to be considered.

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
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.
Define and maintain evaluation frameworks for:
Precision and recall of extracted clinical concepts
Regression testing across model updates
Implement structured output approaches, such as schema-constrained generation and 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 of commercial experience delivering production AI/NLP systems, with experience operating at architectural or technical leadership levels.

Core technical expertise

Strong hands-on experience 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.
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 a willingness to engage deeply with it.
Experience with prompt engineering using structured outputs.
Familiarity with schema-constrained generation, such as 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.
Company pension
25 days of paid annual leave (pro-rata)
A fun and flexible hybrid working environment
Access to an Employee Assistance Programme
A modern, dog-friendly office near Chancery Lane with free drinks