AI Engineers

• Work with business teams to understand processes, challenge assumptions and identify where AI can deliver practical value.
• Build production-quality AI applications, including retrieval-augmented generation, tool-enabled assistants and agentic workflows where appropriate.
• Connect applications to enterprise data, APIs and existing systems, respecting access permissions and information boundaries.
• Develop evaluation approaches covering accuracy, reliability, security, latency and cost.
• Build appropriate human review, exception handling, logging and fallback behaviour into AI workflows.
• Work with platform and DevOps engineers to deploy, monitor and improve applications.
• Contribute reusable components, code reviews, documentation and knowledge transfer as the internal team grows.

• Strong Python engineering skills, with experience building tested, maintainable applications and APIs.
• Evidence of taking AI or machine-learning applications into production and supporting them beyond launch.
• Hands-on experience with LLM integration, retrieval, embeddings and structured outputs.
• Experience delivering in a regulated industry is essential, ideally banking, insurance, investment management or wider financial services.
• An understanding of how sensitive data, auditability, access controls and operational resilience affect engineering decisions.
• Confidence working directly with business stakeholders and explaining technical trade-offs.
• Strong SQL and experience integrating with enterprise data platforms.

Relevant technology experience

Python, SQL, API frameworks such as FastAPI, Git, Docker and automated testing are particularly useful. Experience with Snowflake is highly relevant.