Forward Deployed Engineer

Responsibilities

• 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.
• 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.

Requirements

• 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.

Preferred

• Experience with Azure and/or .NET. We host on Azure and our core stack is .NET, so willingness to integrate with both is required; existing expertise is a bonus rather than a requirement.
• Experience with agent hosting and sandboxing platforms, LLM or MCP gateways, or agentic workflow orchestration tooling.
• Experience in financial services or another regulated environment.
• Experience deploying models built by a data science team into customer-facing production systems.

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

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.

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 Are

• A 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.
• Comfortable in ambiguity.
• You can walk into a department with a vague problem and leave with a scoped, deliverable system, and you are managed by exception rather than by direction.
• A strong partner to non-technical owners.
• Embedding, scoping and communicating with people who own a business process but not the technology is the job, not a distraction from it.
• A platform thinker.
• You have turned one-off solutions into reusable tools or platforms before and you look for the pattern in every engagement.
• Operationally minded.
• You care about monitoring, cost and reliability of what you ship, and about making a prototype into a system someone can rely on.
• Safety-conscious by habit.
• Skills & Experience

Essential

• 5+ 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.
• LLM-powered systems in production: real-world experience working with models via APIs - harnesses, orchestration, structured output, tool use and agents, RAG where appropriate.
• Evals and quality: designing evaluation sets, measuring accuracy, recall and precision for LLM steps, regression-testing prompts and workflows.
• Safety and guardrails in practice: 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.