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
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What Do I Need?

You’ll Bring

• Strong, recent hands-on AI or ML engineering experience, with the technical depth to personally design, build and write production quality solutions using Python
• Clear evidence of personally building, deploying and operating AI or ML systems in production, beyond proofs of concept, experimentation or prototype work, with the ability to explain your individual technical contribution
• Practical experience across the end-to-end AI and ML lifecycle, including data preparation, development, evaluation, deployment, monitoring and ongoing improvement
• Experience building production AI applications and services, including generative AI or agent-based systems, alongside backend services, APIs or data pipelines
• Strong production engineering capability, including practical experience with CI/CD, containerisation, cloud native deployment and observability, together with the technical judgement to make and explain trade-offs across performance, cost, complexity and reliability
• Experience taking meaningful technical ownership within cross functional teams, including applying responsible AI principles such as fairness, transparency, privacy, security and human oversight, while communicating technical decisions clearly to different audiences
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Additional Information

Belonging at The AA