Lead the design and delivery of internal AI tools and systems that support refurbed’s transformation into an AI-native company. As a Senior Backend Engineer for internal AI, you will own the services, integrations, and deployment pathways that take internal tools from prototype to secure, reliable full-scale internal deployment.

You will partner closely with the Head of Internal Technology & AI, who owns the strategy and AI and IT portfolio, to assess technical feasibility, prioritise initiatives, and deliver solutions while collaborating with our core platform Engineering teams. Success will be measured by how effectively internal tools reach and perform , and by clear visibility into what is running, who owns it, what outcomes it drives, and what it costs.

WHO YOU ARE:

• You are an experienced senior backend engineer with a strong track record of building and operating production systems in Go, Python, or a similar language. You are comfortable owning services end to end, from architecture and deployment to secrets management, monitoring, and ongoing reliability.
• You have built AI- or LLM-powered applications for production use. You understand the full lifecycle, including model integration, evaluation, observability, and responding when systems fail in production.
• You are confident working with modern AI development tools and keep up with the rapidly evolving ecosystem. You have hands-on experience with AI coding assistants such as Claude Code, Codex, or Cursor, and understand concepts such as reusable skills, plugins, MCP integrations, agent workflows, and agent orchestration.
• You can evaluate tools and platforms pragmatically and make sound build-versus-buy decisions.
• You exercise good technical and product judgment, including knowing when not to build. You can simplify or stop initiatives when needed and provide clear, constructive feedback on AI-assisted applications, what is ready to ship, what needs improvement, and what should not move forward.
• You work effectively with non-technical stakeholders and treat internal users as customers, bringing a strong product mindset to internal tools alongside technical expertise.
• You take a security-conscious approach by default, considering identity, access control, secrets, and data exposure from the outset. A formal security background is not required.

NICE TO HAVE:

These are advantages, not requirements. We encourage you to apply even if you do not meet every point:

• Experience with Google Cloud Platform
• Strong Go experience or strong Python experience with an interest in working with Go
• Product discovery experience
• Experience with corporate IT, identity and access management, or application security
• Experience supporting an organisation through an AI transformation

Location & eligibility: This role is open to candidates based in Europe (including UK) only with a valid right to work.

WHAT YOU WILL DO: 

Back-end engineering and platform:

• Design, build, and operate reliable back-end services, APIs, and integrations that power our internal AI tools and workflows.
• Manage a secure, supported deployment path that enables non-engineers to launch internal tools with built-in guardrails.
• Build and maintain shared AI capabilities, including reusable skills, plugins, MCP connectors, and a central registry of internal tools, ownership, and costs.
• Deliver company-wide AI services and a standard toolkit that helps every new joiner use AI effectively from day one.
• Evaluate technical options and make pragmatic decisions about architecture, model selection, integrations, and whether to build or buy.
• Help shape the technical direction and scope of our internal AI platform.

Security and governance:

• Help define a risk-based review framework for internally built AI applications and assess solutions against it.
• Implement practical safeguards, including access controls, sandboxing, and controls for model usage and data handling.
• Move business-critical automations from individual machines to reliable, supported infrastructure.
• Establish and continuously improve technical standards, runtimes, and best practices for internal AI development.
• Partner with the Head of Internal Technology & AI to turn governance requirements into practical technical controls.

Enablement and adoption:

• Drive adoption of the tools you build through demonstrations, documentation, and hands-on integration support.
• Partner with departmental AI champions to remove technical blockers and help teams turn ideas into safe, maintainable solutions.
• Document systems clearly so others can operate, maintain, and extend them.
• Stay current with developments in AI models, tools, and engineering practices, and translate relevant advances into practical opportunities for the business.