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.
In your first three months we expect your first departmental engagements to be selected on feasibility and delivered with measurable business value - time saved, cost avoided, risk removed - and at least one ML-built capability deployed to production with proper evals, monitoring and cost controls.
This role is explicitly not ML model training or data science, and it is not a chatbot-prompting generalist: this is production software engineering with AI at its core.
