• Find where AI can remove manual, repetitive or slow work, by sitting with teams across all departments and watching how they work rather than relying on what a request form says.

• Coordinate across departments to scope each initiative: agree what is being solved, who is affected, what data and system access is needed, and who signs off before building starts.

• Design the solution: shape the flow, the screens and the experience so the tool is genuinely usable by non-technical colleagues.

• Build it. Use AI-assisted development tools to go from idea to working prototype in days rather than months, then iterate with real users until it is good.

• Ship internal tools, assistants and automations to production, and take responsibility for whether people use them.

• Build automations and integrations that connect systems, using tools such as n8n and APIs to move data between e-commerce, CMS, ERP, CRM and third-party services.

• Handle the unglamorous parts of a working system: error handling, retries, edge cases, monitoring, and fixing things when they break.

• Evaluate new AI tools and models, run quick tests to see whether they solve a real problem, and recommend what is worth adopting.

• Measure impact. Agree what success looks like before building, then show the time saved or the improvement delivered afterwards.

• Keep stakeholders across departments informed: run working sessions and demos, report progress on active initiatives, and manage expectations on priorities and timelines.

• Drive adoption: demonstrate what has been built, coach colleagues on prompting and usage, and follow up so tools do not go unused.

• Document what you build clearly enough that someone else can pick it up, and support colleagues onboarding onto new tools.

• Work with vendors and development partners where a build is beyond what can be done in-house.

• Ensure all work follows Maison’s IT processes, standards and data handling rules.

• A portfolio of things you have built. This matters far more to us than your degree or your job titles. Side projects, internal tools, automations, prototypes: show us and walk us through them.

• Fluency with AI-assisted development tools such as Claude Code, Cursor, GitHub Copilot, Lovable, Replit or similar, and evidence you can use them to ship real working software.

• Genuine feel for product and user experience: you can tell a good interface from a bad one, and you design for the colleague who will use the tool.

• Solid understanding of how software works end to end (data, APIs, integrations, what breaks and why), whether you learned it formally or taught yourself.

• Strong practical judgement with AI and Large Language Models: structured prompting and knowing where AI genuinely helps versus where it does not.

• Comfort with workflow automation and integration concepts: triggers, conditions, branching, error handling and retries. Exposure to n8n, Zapier, Make or similar is a strong plus.

• Ability to talk to business colleagues, understand what they need, and turn it into something working without a long specification process.

• Curiosity and self-direction. This field changes monthly and we expect you to keep up on your own.

• Good command of English for reading, writing and day-to-day communication.

• No computer science or engineering degree is required. Candidates from any background, including self-taught, are welcome if the work speaks for itself.

• Experience in e-commerce, retail or omnichannel operations is a plus, as is familiarity with platforms such as Haravan or Salesforce Commerce Cloud.