Senior AI Software Engineer

Description

The role centres on designing, building and improving software that gives business, platform and technology capabilities to customers and colleagues.

As a Senior AI Software Engineer, you will design, build and roll out intelligent software that drives innovation, raises engineering productivity and delivers measurable business value.

You will work alongside AI Architects, Data Engineers, Data Scientists, product teams and engineers to create scalable, secure, production-ready AI systems and applications.

The role covers generative AI, AI-assisted code generation, automated testing, faster code review, agentic workflows and improvements to the development process.

You will provide technical leadership, engineering expertise and tooling oversight across the software delivery lifecycle, helping teams use AI responsibly to modernise platforms, improve code quality, streamline delivery and improve developer experience on cloud platforms and technology products.

Responsibilities

• Build high-quality software with suitable languages, frameworks and tools, keeping code scalable, maintainable and fast.
• Work with product managers, designers and engineers to define requirements, choose solution approaches and integrate work with business objectives.
• Review peers' code and share knowledge to build a culture of code quality.
• Follow industry technology trends and contribute to internal technology communities to grow technical excellence.
• Apply secure coding practices to reduce vulnerabilities and protect sensitive data.
• Write unit tests that keep code well designed, readable and reliable.
• Contribute to or set strategy, drive requirements and recommend changes. Plan resources, budgets and policies, maintain processes, deliver continuous improvements and escalate policy breaches.
• If you manage a team, define roles and responsibilities, plan the department's future needs, counsel employees on performance, contribute to pay decisions, lead specialists and balance short and long term goals while keeping budgets and schedules on track.
• If you lead people, show the LEAD behaviours: listen and be authentic, energise and inspire, align across the enterprise, and develop others.
• If you are an individual contributor, act as the subject matter expert in your discipline, guide technical direction, lead multi-year collaborative assignments, bring in other specialisms where needed, and train and coach less experienced specialists.
• Advise key stakeholders, including functional leadership and senior management, on the impact and alignment of work across and within functions.
• Assess and mitigate risks in support of the control and governance agenda, and lead on managing risk and strengthening controls for your team's work.
• Understand how the organisation's functions fit together and work with business-aligned support areas to keep up with business activity and strategy.
• Use sophisticated analysis to compare complex options, define problems and develop innovative solutions, drawing on extensive research.
• Build trusted internal and external relationships, using influence and negotiation to meet key business objectives.

Requirements

• Programming and software engineering: develop, integrate and maintain AI-enabled software in languages such as Python, Java, R and C++, with strong engineering principles, clean code, API design, distributed computing, UNIX tooling and scalable system design.
• Machine learning and AI fundamentals: solid knowledge of supervised and unsupervised learning, neural networks, model evaluation, prompt engineering, code transformation and refactoring, data transformation and schema conversion, automated test case generation and responsible AI practices.
• Data handling and analysis: experience working with large, complex datasets, including preprocessing, feature engineering, data quality assessment, vectorisation, embeddings and data science and machine learning libraries.
• AI engineering and product delivery: experience building AI-powered capabilities into production applications, balancing experimentation with robust engineering, security, observability, maintainability and customer impact.
• Additional skills: tooling selection, developer productivity tooling, Infrastructure as Code with tools such as Terraform and Ansible, CI/CD implementation, automated quality controls and engineering workflow optimisation.
• Additional skills: deploying AI models and AI-enabled services on AWS, Azure or GCP, MLOps, cloud architecture, containerisation, model monitoring and secure production deployment patterns.
• Additional skills: translating complex business and engineering challenges into practical AI solutions, explaining technical outcomes to technical and non-technical stakeholders, and supporting technical planning, implementation and adoption.
• You may be assessed on risk and controls, communication, interaction with a diverse range of stakeholders and job-specific technical skills.

Conditions

Based at the Knutsford office.

All colleagues are expected to live the company's values of Respect, Integrity, Service, Excellence and Stewardship, and its mindset to Empower, Challenge and Drive.