AI Engineer

Qualifications

• What you’ll bring
• Essential Experience
• Practical experience building with AI and agentic technologies: large language models, RAG, prompt engineering, and agent frameworks / orchestration (such as LangChain, LangGraph or Semantic Kernel).
• Hands-on experience using AI coding assistants and agentic development tools (e.g. GitHub Copilot, Claude Code, Cursor) in a professional delivery setting, and the ability to critically review, validate and secure the code they produce.
• Strong hands-on software engineering experience (minimum 10 years commercial), with React and/or Angular, TypeScript, C# / .NET and Python.
• Hands-on commercial experience in Azure cloud, using GitHub and Azure DevOps, across all phases of the SDLC and Agile delivery.
• Proven ability to rapidly prototype and validate ideas, moving a concept from whiteboard to working demo in days.
• Demonstrable experience leading, influencing and mentoring through hands-on contributions.
• Experience of working in a financial services organisation, preferably with exposure to the insurance industry / insurance broking. Where this directly relevant experience is not available, we will be looking for an ability to rapidly become effective in a new, unfamiliar environment.
• First-rate communication skills, applied to rapid and creative problem solving with technical and business stakeholders in an occasionally pressurised environment.
• Comfortable working on your own initiative or as part of a broader project team, remotely or face to face, and confident challenging established approaches.
• Desirable Experience:
• Experience running AI / LLM solutions in production at scale, including evaluation frameworks, guardrails and cost management.
• Experience designing or orchestrating multi-agent workflows, and familiarity with agent protocols (e.g. MCP) for integrating AI agents with engineering pipelines and enterprise data.
• Experience with microservice based architectures and containerisation.
• Experience building and working with mathematical algorithms, or with geospatial, catastrophe or financial-modelling data.
• Experience understanding and translating complex business processes across multiple stakeholder groups.