AI Automation Analyst

What you'll do

You will design, build and deploy AI agents and automations that make a real difference to how we work.

Across our organisation, too much time still goes on work that is manual, repetitive or slow. As our AI Automation Intern, you will find the workflows where AI can make the biggest difference, then build practical AI agents, automations and integrations that people use every day. Working with Python, LLMs, APIs and databases, you will take ideas from proto@type to production and show the measurable impact they have. This is not a theoretical research project. It is a hands-on build role, focused on real deployment in a live organisation.

The Role. You will work across the business to understand how current workflows operate, identify high-value opportunities for AI and automation, and design, build and test solutions with the people who will use them. Examples could include AI agents that handle administrative workflows end to end; automations that connect our systems through APIs and remove manual data entry; retrieval-augmented generation (RAG) assistants that give teams fast, accurate answers from our policies, procedures and knowledge bases; tools that pull, clean and report on data from our databases; and LLM-powered agents that help teams draft documents, correspondence and marketing content.

What You Will Be Doing. Spending time with teams across the business to understand how work gets done and where the inefficiencies are. Identifying tasks that can be automated or significantly improved with AI, and prioritising those with the greatest value. Designing and building AI agents and automations in Python, using LLMs and agent-based workflows to complete multi-step tasks. Integrating with internal and third-party systems through APIs, and reading from and writing to databases to keep information accurate and connected. Building RAG pipelines that ground LLM responses in our own documents and data, with a focus on accuracy and reliability. Managing your code with version control in GitHub, using branches, pull requests and clear documentation so your work can be reviewed, maintained and built upon. Testing your solutions with real users, gathering feedback and improving them quickly. Making sure everything you build handles personal and sensitive information safely and in line with data protection requirements. Measuring and reporting the impact of your work, such as time saved, faster turnaround and improved consistency or quality.

What Success Looks Like. By the end of your placement, you will have delivered several working AI agents or automations, reduced manual effort in important workflows, improved speed, consistency or quality for our teams, built solutions that can be scaled across the business, and demonstrated clear, practical impact from your work.