AI & Data Engineer

In this role, you will design, build and productionise AI agents, RAG capabilities and data products to support Balance Sheet Modelling for a leading Pension and Investments client. You’ll work with business, architecture and technology teams to translate use cases into scalable, secure AI and data solutions. You’ll deploy, monitor and support enterprise AI, driving reliable production-grade outcomes. This is a remote-first opportunity with travel to client sites, offering impact at scale in financial services.

• Design, develop and deploy production-ready AI agents and agentic workflows
• Build RAG solutions integrating LLMs with enterprise data and knowledge sources
• Apply prompt engineering to improve AI accuracy and reliability
• Translate business requirements into robust technical solutions
• Build and maintain scalable data pipelines and data products for Balance Sheet Modelling and AI use cases
• Develop reusable AI, data and integration components for multiple solutions
• Design and integrate APIs connecting AI with internal apps, data sources and third-party services
• Develop workflow automation using Power Platform and Copilot Studio
• Work with Azure Databricks and/or Microsoft Fabric to process and manage data
• Implement testing, DevOps, monitoring and operational controls for production readiness
• Produce technical documentation covering design, deployment, configuration and support
• Collaborate with AI architects, data teams, engineering teams and business stakeholders throughout the lifecycle

• Strong hands-on AI engineering experience, especially building AI agents and agentic workflows
• Practical experience with LLMs, prompt engineering and RAG architectures
• Proficiency in Python and SQL
• Experience with Azure Databricks and/or Microsoft Fabric
• Solid understanding of data engineering, data pipelines and API integration
• Experience developing automation solutions using Microsoft Power Platform and Copilot Studio
• Experience taking AI or data solutions from PoC to production
• Knowledge of DevOps, CI/CD, automated testing, monitoring and production support
• Ability to translate business use cases into scalable technical solutions

• collaboration with cross-functional teams
• clear communication with stakeholders
• problem solving and analytical thinking
• AI Agents and agentic workflows
• LLMs and prompt engineering
• RAG architectures