Senior AI Deployment Engineer

 

 

Moneybox serves more than 2M customers and runs a live service handling over 20M API requests a day. We have agreed a company-wide AI Platforms strategy and are building a new AI Deployment team to deliver on it. This is the first of several Senior AI Deployment Engineer hires, reporting to the Head of AI Platforms & Deployment.

You will be a forward-deployed senior engineer who unlocks AI-driven solutions to business problems: an expert in deploying AI and using it safely, not an ML modeller. The work is mainly Python across the modern AI engineering stack - harness engineering, skills and tool building, agent workflows and orchestration, agent hosting and sandboxing, guardrails, evals, RAG and context engineering, and tokenomics (cost, latency, model selection). Production-grade LLM system experience is the core requirement.

You will work on three types of project:

• Departmental engagements. Embed with departments to AI-enable tasks and processes in a more sophisticated way than "just ask Claude" - for example, Python pipelines where one step is an LLM API call - delivering real incremental value with each engagement and transforming working patterns into load-bearing, AI-enabled business processes.
• Customer-facing AI deployment. Deploy and integrate AI components built by our ML and Decisioning teams into production: the engineering implementation layer between a working model and a live customer feature.
• AI platform capabilities. Work with the AI Platforms team to turn engagement patterns into safe, increasingly self-serve company-wide tooling.

Departments across Moneybox are already building AI tools themselves - we want to provide them with a safe path to load-bearing use at scale. This role catches that demand and matures it properly.