Senior AI Engineer

HealthHero; United Kingdom; On-site; GBP 110,000 - 150,000; Full time

Job summary

HealthHero is seeking a Senior AI Engineer to advance AI initiatives from prototype to production. The first project, the Clinical Brain, combines an AI layer with deterministic components inside a Class II medical device, demanding rigorous safety, traceability, and documentation.

You will partner with the Director of Clinical Products, Head of Quality & Compliance and cross-functional teams to shape AI strategy, design architecture, run clinical validation, and implement scalable, auditable AI

Qualifications

• 4+ years of software engineering experience with at least 2 years shipping LLM or ML-based features to production.
• Strong TypeScript (codebase is TypeScript/.NET) plus solid Python for AI/data work.
• Comfortable with APIs, cloud services (AWS, GCP or Azure), containers and CI/CD.
• Hands-on experience with LLM APIs (OpenAI, Anthropic, Mistral or similar) and RAG/tool-use frameworks.
• Proven track record building evaluation pipelines (test sets, metrics, LLM-as-judge) with experience in mixtures of evaluators and calibration against clinician labels.

Responsibilities

• Own the technical delivery of AI initiatives end-to-end, from design to production deployment.
• Collaborate with Product, Data, Engineering, Clinical, Operations and Customer Service teams.
• Contribute to AI roadmap, architectural decisions and engineering best practices.
• Translate clinical needs into AI behaviour and design clinical validation.
• Work with Quality & Compliance on documentation, risk management and traceability.

Skills

TypeScript

Python

APIs

Cloud platforms

CI/CD

LLM APIs

RAG frameworks

Evaluation pipelines

Prompt engineering

Tools

OpenAI

Anthropic

Mistral

Job description

We have a hugely exciting opportunity for a Senior AI Engineer who will take HealthHero's AI initiatives from prototype to production.

Your first project will be our flagship, the Clinical Brain: a system that includes a Class II medical device, with an AI layer interacting with deterministic components. You will work on it directly with the Director of Clinical Products and the Head of Quality & Compliance, so you will need to be comfortable building to the rigour, safety, traceability and documentation that a regulated medical device environment requires.

This is a cross-functional role, working across Data, Product, Engineering, Clinical, Operations and Customer Service teams. You will collaborate closely with Engineering and Data Platform team to build scalable AI capabilities, and with Data Scientists and clinical experts to design robust evaluations and ensure the quality, reliability and safety of AI solutions. Your key stakeholders will be the Head of AI Products, and, on the Clinical Brain, the Director of Clinical Products and the Head of Quality & Compliance.

You will own the technical delivery of AI initiatives end to end, from designing solutions and making architectural choices to building, evaluating and deploying them into production. You will also contribute to shaping our AI engineering practices and technical roadmap.

Your work will give clinicians time back, help patients access the right care faster, and make our teams more efficient, while maintaining the highest standards of clinical quality, patient safety and data protection.

About the role

Drive AI strategy and technical leadership

• Partner with Product, Data, Engineering, Clinical and Operations teams to identify, scope and evaluate AI opportunities.
• Challenge requirements and technical approaches, assess feasibility, business value and costs, and help prioritise high-impact initiatives.
• Contribute to the AI roadmap, architectural decisions and technology choices, while helping define AI engineering best practices and sharing expertise across teams.

Your first project: the Clinical Brain

• Abstract patient intent: turn free-text and conversational patient input into structured, clinically meaningful intents.
• Condense and cluster questionnaires: reduce and group clinical questionnaires while preserving clinical meaning and safety.
• Combine NLP, LLMs and machine learning: design the AI layer and its interfaces with the deterministic components so that every output is traceable and explainable, with patient safety built in.
• Optimise the clinical graph: improve the structure and performance of the knowledge graph that underpins the Clinical Brain.
• Work with the Director of Clinical Products to translate clinical needs into AI behaviour, and to design and run clinical validation.
• Work with the Head of Quality & Compliance to meet Class II medical device requirements: design documentation, risk management, change control and traceability from requirement to test.

Evaluation

• Golden datasets: curate and maintain reference datasets with clinicians, covering common cases, edge cases and safety-critical scenarios.
• Mixtures of evaluators: go beyond a single LLM-as-judge by combining multiple judges, calibrating them against clinician review and measuring inter-rater agreement.
• Human-in-the-loop review: design efficient clinician review workflows that feed back into datasets, prompts and models.
• Regression testing: automate evaluation so that every prompt, model or data change is gated before release.
• Evidence for regulation: produce evaluation evidence that supports the Class II medical device documentation within the Clinical Brain, and monitor performance and safety once in production.

Essential (must haves)

• 4+ years of software engineering experience, including at least 2 years shipping LLM or ML-based features to production.
• Strong TypeScript (our codebase is mostly TypeScript and .NET), plus solid Python for AI and data work.
• Comfortable with APIs, cloud services (AWS, GCP or Azure), containers and CI/CD.
• Hands-on experience with LLM APIs (OpenAI, Anthropic, Mistral or similar) and frameworks for RAG, agents and tool use.
• Proven track record building evaluation pipelines (test sets, metrics, LLM-as-judge) and using the results to drive decisions, with experience of or strong interest in methods beyond a single judge, such as mixtures of evaluators, calibration against clinician labels and inter-rater agreement.
• Solid prompt engin