Senior AI Deployment Engineer

• Own engagement delivery end to end: scope with the department, design the solution, build it, deploy it, and agree the handover and ownership model.
• Define the problem from vague departmental pain points and turn them into scoped, deliverable systems.
• Engineer AI solutions properly, including pipelines, LLM API integration, evals, guardrails, monitoring, and cost and accuracy optimisation.
• Decide when a step must be deterministic and when an LLM is the right tool.
• Graduate shared, team-load-bearing tools into owned business systems under a full SDLC when the value justifies it.
• Deploy ML-built components into production, including serving, integration with the platform, and surrounding engineering in partnership with Decisioning and Data Science teams.
• Build reusable capability by converting engagement learnings into shared tooling, templates, playbooks, and self-serve workflows on the AI Platforms stack.
• Develop platform components including guardrails, sandboxing, workflows, and gateways.
• Work alongside embedded specialists during the team ramp-up and absorb their output so the capability stays with Moneybox.
• Deliver early departmental engagements with measurable business value such as time saved, cost avoided, or risk removed.
• Deploy at least one ML-built capability to production with proper evals, monitoring, and cost controls in the first three months.

Technologies:

• AI
• API
• Azure
• CI/CD
• Embedded
• LLM
• MCP
• Python
• RAG
• ASP.NET
• Cloud
• Model Training, We are Moneybox, an award-winning wealth management platform with a mission to give everyone the means to get more out of life. We help more than 1.5 million people build wealth throughout their lives, whether they are saving and investing, buying their first home, or planning for retirement. We serve more than 2 million customers and run a live service handling over 20 million API requests a day. We are building a new AI Deployment team as part of our company-wide AI Platforms strategy, and this role is the first of several Senior AI Deployment Engineer hires. You will work closely with the Head of AI Platforms & Deployment, the AI Platforms team, Decisioning, Data Science, and engineering squads to deliver production AI solutions, customer-facing AI deployments, and reusable platform capabilities on Azure and .NET.

• 5 years of software engineering with meaningful production ownership.
• Built and shipped LLM-powered systems that ran in production.
• Production-grade Python as our primary language.
• Real-world experience working with models via APIs, including harnesses, orchestration, structured output, tool use, agents, and RAG where appropriate.
• Experience designing evaluation sets and measuring accuracy, recall, and precision for LLM steps.
• Experience regression-testing prompts and workflows.
• Practical experience with safety and guardrails, including PII handling, data-boundary discipline, prompt-injection awareness, and human-in-the-loop design.
• Experience with cost and performance optimisation, including model selection, caching, batching, token economics, and latency budgets.
• Experience with deployment and operations, including CI/CD, containerisation, monitoring, and alerting for AI workloads.
• Data processing fundamentals, including pipelines, transformation, validation, and anomaly handling.
• Customer- or stakeholder-facing delivery experience such as consultancy, forward-deployed engineering, solutions engineering, or embedded or platform roles serving non-engineering users.
• Experience with Azure and/or .NET is desirable, and willingness to integrate with both is required.
• Experience with agent hosting and sandboxing platforms, LLM or MCP gateways, or agentic workflow orchestration tooling is desirable.
• Experience in financial services or another regulated environment is desirable.
• Experience deploying models built by a data science team into customer-facing production systems is desirable.