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# AI & Data Engineer

- **Company:** Reactive Markets
- **Location:** UK (Remote available)
- **Salary:** £59,093.0
- **Contract:** Permanent contract
- **Skills:** Web Interfaces, JavaScript (Programming Language), Artificial Intelligence, Amazon Web Services, Big Data, Linux, Python (Programming Language), PostgreSQL, SQL Databases, TypeScript, Management of Software Versions, Large Language Models, Prompt Engineering, Git, Kubernetes, Vertica, Api Design, Docker
- **Published:** August 11, 2026
- **Apply:** https://www.adzuna.co.uk/jobs/details/5834881246

## About the Role

* End-to-end systems thinking - you reason about the whole system, not just your part of it: how it behaves under load, how it fails, what depends on it, and who is affected when it is wrong
 * Strong Python - production Python that other people depended on, with real views on packaging, dependency management, testing and typing. First-language Python is what we would most like to find
 * The ability to work with a business function - evidence you have embedded with domain experts who are not engineers, learnt their world, and earned the right to change how things work
 * SQL - comfortable writing analytical queries against large datasets (ClickHouse, PostgreSQL or similar)
 * Git and Linux fundamentals

Important:

 * Library and API design - versioning, backwards compatibility, and designing from the caller's side
 * Production discipline - tests, monitoring, reproducibility, and fixing causes rather than symptoms
 * Docker, Kubernetes and AWS - your work runs in production infrastructure

Welcome, and we will help you grow it:

 * Go - cross-training is expected; prior experience is a bonus, not a requirement
 * LLM APIs and tooling - Claude, OpenAI or similar; prompt engineering, tool use, MCP
 * TypeScript / JavaScript - useful for Slack integrations and web interfaces
 * Financial services experience - a plus but not essential. Domain knowledge can be learned; engineering instinct cannot

## Description

* You ship. You iterate quickly, get things in front of users, and improve based on feedback
 * You respect other people's expertise. The analyst who wrote the notebook knows something you do not. Your job is to make their work durable, not to correct their taste
 * You own it. When you build something, you take responsibility for its behaviour in production - you monitor it, you fix it, you make it better
 * You are honest when a number is wrong. You tell the people who acted on it before you quietly correct it
 * You write things down and communicate. We are distributed across time zones, and documentation is how we scale knowledge - every well-written page also makes our AI tooling better
 * You embrace AI as a tool. You use it to amplify your own work, and hold generated code to the same standard as anything else


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