AI Engineer

Job overview

This role will lead the development, deployment and operationalisation of next-generation, agentic AI-driven services to address real-world healthcare challenges across the Northwest of England. Working within a secure, high-availability analytics research environment (KARECTL), the post holder will collaborate closely with clinical, academic, data engineering and infrastructure teams to translate AI research outputs into safe, reliable and impactful operational solutions.

The post holder will play a key role in bridging research and clinical operations, ensuring that emerging AI and large language model (LLM) capabilities are deployed responsibly, at scale, and in line with NHS Digital, GDPR and Information Governance requirements. This is an opportunity to work at the intersection of AI, health informatics and clinical practice, contributing directly to improved patient care and service delivery.

Main duties of the job

• Design, deploy and optimise agentic AI workflows within a secure, containerised analytics environment
• Develop and maintain scalable data pipelines supporting AI development, deployment and monitoring
• Establish and operate robust ML/LLMOps processes to support reliable, high-quality AI inference
• Work collaboratively with academic researchers to operationalise AI and LLM research outputs
• Integrate agentic AI solutions into existing NHS clinical and operational workflows
• Engage with data and infrastructure teams to implement solutions across the wider KARECTL ecosystem
• Ensure all AI solutions align with organisational strategy, clinical priorities and governance requirements
• Implement monitoring, observability, audit and reporting mechanisms for deployed AI agents
• Produce clear technical documentation and deployment guidance for all solutions delivered

Detailed job description and main responsibilities

The post holder will be responsible for the end-to-end design, deployment and operational management of agentic AI solutions within a secure, high-availability, cloud-native analytics environment. A core requirement of the role is the use of Kubernetes-based container orchestration to deploy, scale and manage AI services, data pipelines and supporting infrastructure in line with organisational standards and best practice.

The role will involve building and maintaining containerised AI and LLM services, configuring Kubernetes workloads, and working with platform and infrastructure teams to ensure solutions are resilient, observable and secure. This includes implementing deployment patterns, service monitoring, logging, and automated scaling to support reliable day-to-day clinical and operational use.

The post holder will work closely with academic researchers and clinicians to translate AI research prototypes into production-ready, Kubernetes-deployed services, ensuring that solutions are robust, reproducible and capable of operating at scale. They will integrate agentic AI capabilities into existing NHS workflows, engaging stakeholders to gather requirements and iteratively refine solutions based on real-world feedback and performance data.

They will be responsible for establishing and operating ML/LLMOps processes that support version control, validation, monitoring and lifecycle management of deployed models within containerised environments. The role also requires ensuring compliance with NHS Digital, GDPR and local Information Governance frameworks, including the implementation of access controls, audit logging and documentation suitable for clinical assurance.

The post holder will contribute to the development of observability, reporting and assurance mechanisms to demonstrate the reliability, fairness and trustworthiness of AI outputs. They will lead on producing clear technical documentation, deployment guides and operational runbooks to support handover, sustainability and continuous improvement of Kubernetes-based AI services.