Why Join Us?

• Annual bonus, cash car allowance and private medical insurance
• Build and take ownership of production AI and machine learning solutions, working hands on from initial problem definition through to deployment, monitoring and continuous improvement
• Work across a broad range of AI use cases including predictive analytics, optimisation, automation, generative AI and agent based systems, solving real business and customer problems at scale
• Help shape how AI engineering is delivered across The AA, contributing reusable patterns, engineering standards and responsible AI practices while working alongside experienced Data, Product and Technology teams
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For over a century, The AA has been keeping Britain moving. Today, millions of drivers trust us to be there when it matters most, and we're equally committed to supporting the people who make that possible. With a 4.5-star Glassdoor rating, we've built a culture where colleagues are empowered to grow, collaborate and make a real impact for our customers on every journey. Which has been recognised by Sunday Times “Best Places to Work” for the last two years.

Job Description

This is the Job

We’re looking for an AI Engineer who has genuinely built AI and machine learning systems, not simply worked alongside them. This is a highly hands on engineering role where you’ll design, write, test, deploy and improve production AI solutions, taking technical ownership from the initial problem through to live operation and ongoing optimisation.

You’ll work across predictive analytics, optimisation, automation, generative AI and agent-based systems, collaborating with data scientists, product managers, engineers and business stakeholders. Whether owning an outcome end to end or a defined technical area within a wider programme, you’ll be expected to understand how the technology works, make sound engineering decisions and be able to explain the choices you have made.

We’re particularly interested in the work you have personally delivered. Your application should clearly demonstrate AI or ML systems you have built, deployed and operated in production, including your individual technical contribution and the impact achieved.

Key Responsibilities

• Design, build, test, deploy and operate scalable AI and ML applications, services and data pipelines using clean, maintainable Python
• Own technical outcomes from problem definition and evaluation through to production deployment, monitoring and continuous improvement
• Make pragmatic engineering decisions across model performance, latency, cost, complexity, reliability and maintainability
• Build robust production capability using CI/CD, containerisation, cloud native practices, monitoring, logging, evaluation and deployment automation
• Develop reusable components and engineering patterns, contributing through code reviews, technical guidance, documentation and knowledge sharing
• Embed responsible AI principles throughout development, including fairness, transparency, privacy, security, evaluation and appropriate human oversight