AI Backend Engineer, EMEA

About The Role We are looking for a Backend Engineer who builds with AI at the centre of their craft — not as a novelty, but as a primary tool that shapes how they design, write, review, and ship code. If you've already integrated Claude Code, agentic coding tools, and MCP servers deeply into your day-to-day, and you have opinions about how to do it well, we want to talk. You'll join a growing business with real engineering problems: a cloud-based property and content management platform with 11 integrations and more on the way, plus a new Booking & Request Management Platform with real-time data and predictive intelligence features. Your job is to ship meaningful backend work — fast — and to bring the rest of the engineering team along with you on AI-augmented practices. We are language-agnostic. Our current stack is PHP (Symfony) and Node.js, but we'll happily take a strong engineer from Python, Go, Java, C#, Ruby, or elsewhere who can move quickly between languages — especially with AI tooling at their side. The bar is engineering judgement and AI fluency, not a specific language on your CV. This role is open to EMEA based candidates. What This Role Actually Looks Like You start a feature by thinking carefully about scope and interfaces, then orchestrate Claude Code (or equivalent) to scaffold and iterate. You've written CLAUDE.md / AGENTS.md files that materially improve agent behaviour, and you know why they work. You configure MCP servers to give agents the right context — internal docs, ticketing, databases, observability — and you have a view on what should and shouldn't be exposed. You review AI-generated code with the same rigour as human-written code, and you can spot the failure modes that less experienced AI users miss. You measure and talk about the impact: cycle time, defect rate, review burden, the things that actually matter. You help other engineers level up — pairing, internal docs, lunch-and-learns, custom commands, whatever moves the needle. Key Responsibilities AI-First Development: Lead by example in using Claude Code and agentic tooling for real production work. Establish patterns the team can follow. Agent Configuration & MCP: Build and maintain agent instruction files, custom commands/slash-commands, sub-agents, and MCP server integrations that make AI tooling genuinely useful on our codebase. AI Workflow Design: Define how AI-generated code enters our repos — review standards, test expectations, security checks, and the guardrails around it. Backend Engineering: Ship real backend features and services. AI accelerates the work; it doesn't replace the engineering. Code Quality & Review: Set the bar for what good looks like in an AI-augmented codebase. Peer-review human and AI-authored code with equal scrutiny. Team Enablement: Upskill other engineers in AI tooling — share patterns, write internal guides, run sessions. Collaboration: Partner with product, frontend, DevOps,…