Deployment Engineer in London

London OfficeProduct Engineering – AI Platforms /Full Time /HybridAbout MoneyboxAt Moneybox, our mission is to give everyone the means to get more out of life. 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 BriefMoneybox 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). You will work on three types of project: 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. Scope with the department, design the solution, build it, deploy it, and agree the handover and ownership model - from prototype through to stable production. Engineer AI solutions properly. Pipelines, LLM API integration, evals, guardrails, monitoring, and cost and accuracy optimisation for the systems you build. Graduate tools into business systems. 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. 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. 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. Who You AreA production engineer with AI at the core. You have built and shipped LLM-powered systems that ran in production and can talk concretely about evals, failure modes, cost curves, and what you would do differently. You care about monitoring, cost and reliability of what you ship, and about making a prototype into a system someone can rely on. PII handling, data-boundary discipline, prompt-injection awareness, human-in-the-loop design and graceful failure are how you build, not a checklist you apply afterwards. Skills & ExperienceEssential5+ years of software engineering with meaningful production ownership. Has built and shipped LLM-powered systems that ran in production. Production-grade Python as your primary language. Evals and quality: designing evaluation sets, measuring accuracy, recall and precision for LLM steps, regression-testing prompts and workflows. PII handling, data-boundary discipline, prompt-injection awareness, human-in-the-loop design. Cost and performance optimisation: model selection, caching, batching, token economics, latency budgets. Deployment and operations: CI/CD, containerisation, monitoring and alerting for AI workloads. Data processing fundamentals: pipelines, transformation, validation, anomaly handling. Customer- or stakeholder-facing delivery experience: consultancy, forward-deployed engineering, solutions engineering, or embedded or platform roles serving non-engineering users. NET. NET, so willingness to integrate with both is required; existing expertise is a bonus rather than a requirement. Experience in financial services or another regulated environment. Experience deploying models built by a data science team into customer-facing production systems. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. If you would like more information about how your data is processed, please contact us.