AI Engineer

AI Engineer

About the role

Engage builds software that helps people across an organisation find information, communicate and get things done. Our customers include businesses with large, distributed and frontline teams. We are now expanding what that software can do through AI, building on a live platform and real customer problems.

We are looking for a strong software engineer who already uses AI to build better software and has delivered AI features that people use. You will work across the product, from application code and integrations to AI assistants and automated workflows. You will own the engineering needed to turn a promising idea into something dependable.

This is a hands-on role with room to shape how we build. You will work closely with product and engineering, make practical technical decisions and help the team improve its use of AI.

Your remit covers practical software delivery and production AI within the Engage product. You will collaborate on architecture and reusable capabilities, while keeping ownership of working code and its behaviour after release. Research and experimentation should lead to a useful product outcome.

What you'll be doing

• Deliver product features across APIs, services, data and user-facing workflows, using sound software engineering alongside AI.
• Build AI features that use the right context, tools and permissions to solve a clear user problem. Choose a simpler software approach when that is the better fit.
• Take work from discovery and a working prototype through testing, deployment, monitoring and improvement.
• Use coding agents and other AI tools throughout development, with clear tasks, useful context, code review and independent verification.
• Build reusable approaches to prompts, retrieval, tool use and structured outputs where they improve the product.
• Share what works with the team and explain where AI-generated code or model behaviour needs closer scrutiny.
• Create tests and evaluation examples that expose failures, including misleading answers, incorrect tool calls and permission errors.
• Monitor quality, latency and cost as usage grows. Diagnose failures and put appropriate limits, retries, fallbacks and human review in place.
• Protect customer data and tenant boundaries, and make important behaviour traceable and supportable.

What we're looking for

• Strong experience delivering and maintaining production software, with ownership beyond the initial release.
• Evidence of an AI feature or workflow you personally built, including the choices you made and what changed after real users tried it.
• Confidence writing and reviewing production code, including Python for AI work, and working with APIs, data stores and cloud services.
• Regular, thoughtful use of AI development tools. You can explain how you check their work and recognise when they are wrong.
• Practical understanding of testing, debugging, access control and the reliability challenges of systems that depend on model outputs.
• Good judgement about scope and trade-offs, and the ability to explain technical decisions clearly to product and engineering colleagues.

Useful experience

• Experience with SaaS products, enterprise integrations or software used by distributed teams.
• Experience with retrieval, model evaluation, agent tool use or managing model cost and performance in production.
• A record of improving engineering practices or helping other developers adopt useful tools.

Why Engage

You will build on a product with established customers, with direct access to the people defining its next stage. There is scope to influence both the features we create and the way engineering works, and to see the effect of your decisions in real customer use.

What success looks like

• First three months: Understand the product and customer context, ship a useful improvement and demonstrate an AI development workflow the team can trust. Establish clear tests and monitoring for the work you own.
• By six months: Own meaningful product capability in production, show how you have improved its quality or efficiency, and turn lessons from delivery into approaches other engineers can use.
• Over the first year: Become a trusted technical owner as the platform grows, taking on broader responsibility through the quality of your delivery, judgement and contribution to the team.