Forward Deployed Engineer

About Moneybox At Moneybox, our mission is to give everyone the means to get more out of life. We're guided by our belief that wealth isn't about the money, it's about the means to more - more freedom, opportunities, possibilities, and peace of mind. Moneybox is an award-winning wealth management platform, helping over one and a half million people build wealth throughout their lives, whether they’re saving and investing, buying their first home, or planning for retirement. Job Brief 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. What You'll Do • Own engagement delivery end to end. Scope with the department, design the solution, build it, deploy it, and agree the handover and ownership model - from prototype through to stable production. Engagements arrive as vague pain; you define the problem, not just the solution. • Engineer AI solutions properly. Pipelines, LLM API integration, evals, guardrails, monitoring, and cost and accuracy optimisation for the systems you build. Know when a step must be deterministic and when an LLM is the right tool. • Graduate tools into business systems. Take shared, team-load-bearing tools that people have built for themselves and rebuild them as owned business systems under a full SDLC where the value justifies it. • Deploy ML-built components into production. Serving, integration with the Moneybox platform, and everything surrounding the model, in partnership with Decisioning and Data Science teams (who own what happens inside the model) and our engineering squads. • Build reusable capability. Convert engagement learnings into shared tooling, templates, playbooks and self-serve workflows on the AI Platforms stack. Building out platform components including guardrails, sandboxing, workflows, and gateways. • Raise the bar. Work alongside embedded specialists during the team's ramp-up, absorbing and internalising their output so the capability stays with Moneybox. In your first three months we expect your first departmental engagements to be selected on feasibility and delivered with mea