AI Software Engineer

CONTEXT

Our client, a major player in its sector, has built an internal AI platform intended to accelerate design and value-stream workstreams. The platform is at an inflection point: it has proven useful in pockets, but it must now become a trusted, scalable capability used by digital professionals across the organisation. The business wants more than a model or a demo; it wants a product that translates messy needs into clear technical requirements, validates solutions with real users, and drives adoption without losing sight of governance and value. As AI Software Engineer, you will be part of the story that turns experiments into everyday practice. You will sit between business ambition and engineering reality, helping teams understand what is possible, what is sensible, and what will create durable business value. The role is part builder, part translator and part change agent.

ACTIVITIES

• Partner with business and design stakeholders to uncover needs, pain points and opportunities, then shape them into technical requirements and acceptance criteria.
• Design, prototype and validate AI solutions, including retrieval, prompt orchestration, evaluation, and integration patterns for internal platform services.
• Develop and improve platform features with clean, maintainable code, documentation and automated tests.
• Run technical proof points and solution reviews, assessing feasibility, quality, risk and business value.
• Guide digital professionals through implementation choices, onboarding paths and best practices.
• Support adoption through demos, training material, office hours and feedback loops.
• Collaborate with product, data, security and platform teams to ensure solutions fit architectural and compliance expectations.
• Capture learnings and turn recurring needs into reusable components or platform improvements.

EXPECTED ACHIEVEMENTS

• A measurable increase in platform adoption among digital teams and a stronger pipeline of valuable use cases.
• Clear technical requirements and validated AI patterns that reduce rework and speed delivery.
• Reusable platform components, guardrails and documentation that make AI solution-building more consistent.
• Confident users who understand how to apply AI safely and effectively in their daily work.
• A visible contribution to business value, from faster design cycles to richer insight and better decision support.

ENVIRONMENT

• Modern AI engineering stack with Python, LLM APIs, prompt orchestration, retrieval and evaluation tooling.
• Internal AI platform, APIs, cloud services, version control, CI/CD and observability practices.
• Product and design collaboration methods, discovery workshops, user feedback loops and agile delivery.
• Governance, security and data-protection principles applied to AI use cases.
• A cross-functional environment spanning design, value streams, data, product and technology teams.