Senior AI Engineer

I'm working on an opportunity with an established, fast-growing AI technology business that's building production-grade AI solutions for the financial services industry.

They're moving beyond AI experimentation and building intelligent systems that are changing how banks, advisers and financial institutions operate. With strong investor backing, major financial services partnerships and growing demand for their technology, they're now looking to bring in a Senior AI Software Engineer to help shape the next stage of their platform.

This is a hands-on engineering role where you'll take ownership of AI-powered products from initial design through to production deployment. You'll work across backend services, frontend applications, cloud infrastructure and AI integrations, helping turn LLM capabilities into reliable products that solve real business problems.

You'll be working with LLMs, RAG and agentic workflows, designing the systems and integrations around them rather than building or training models from scratch.

There's plenty of scope to influence architectural decisions, improve engineering standards and help determine how AI products are built and delivered as the business scales.

• Building and deploying production AI applications using LLMs, RAG and agentic workflows.
• Developing scalable backend services and APIs using Node.js and Type Script.
• Building modern, customer-facing applications using React.
• Designing cloud-native systems and integrations across AWS.
• Improving AI reliability through evaluation, monitoring and observability.
• Taking ownership of features end-to-end, from architecture and development through to deployment and ongoing optimisation.
• Working closely with product and engineering teams while helping mentor other engineers and shape technical direction.

• Strong software engineering fundamentals and experience delivering production applications.
• Hands-on experience building and deploying LLM-powered products.
• Strong Node.js, Type Script and React experience.
• Experience with RAG, prompt engineering, AI agents or LLM orchestration.
• Solid AWS knowledge and experience with APIs, microservices and distributed systems.
• An engineering mindset focused on quality, reliability, performance and maintainability.

This is a chance to work on AI products with genuine commercial applications, rather than spending your time on proofs of concept that never make it into production.

You’ll have meaningful ownership across the stack, the opportunity to influence architecture and engineering decisions, and the chance to help build out technology that’s being adopted within financial services.

If you’re a strong software engineer who has moved into AI and wants to work on products where the engineering challenges go well beyond simply connecting an LLM to an API, this is well worth a conversation.