Head of AI Platforms & Deployment

• Own the AI platform strategy, including risk management, horizon scanning, supplier selection and vendor management, business case ownership, and benefits realisation through a coherent, business-driven delivery roadmap.
• Build and lead the AI Deployment team by hiring, line-managing, and setting engagement priorities and delivery standards for forward-deployed AI engineers.
• Grow into managing both the platform and deployment sub-teams as the function expands.
• Own perimeter safety in an AI-native world and set the defensive AI strategy in collaboration with Tech Ops.
• Own AI platforms and costs, including harnesses, tooling, cost management, forecasting, optimisation, and the staff access model.
• Deliver AI safety for internal use through sandboxing, policy, integrations, MCP governance, usage guardrails, and monitoring expectations.
• Build and evolve the platform pillars: control, monitoring and sandboxing; agentic workflow orchestration; and knowledge capture and organisation.
• Own the AI workflow tooling and the tiered graduation ladder from personal tool to shared tool to business system.
• Evolve existing deployments tactically so they align with the strategic direction.
• Own model hosting and scaling for AI and model workloads, including the customer-facing models behind our Aurora financial guidance service, in collaboration with Tech Ops.
• Enable departments to use AI safely today by providing clear guardrails, fast risk assessment, and a practical path for demand to come through the front door.

Technologies:

• AI
• API
• Azure
• Cloud
• LLM
• MCP
• Python
• Security
• ASP.NET
• Architect
• Embedded

More:

We are partnering directly with Moneybox to hire this newly created first role in a company-wide AI Platforms function. Moneybox is an award-winning wealth management platform with a mission to help everyone get more out of life, serving over 1.5 million people and supporting savings, investing, home buying, and retirement planning. Our engineering team serves more than 2 million customers and handles over 20 million API requests a day. This is a hands-on player-manager position reporting to the Engineering Director, with responsibility for both the AI Platforms capability and the AI Deployment team. We need someone who can work quickly, build the function from scratch, and deliver meaningful platform improvements within the first six months while shaping a multi-year strategy. We value pace, learning, strong cross-functional leadership, and the ability to help the business use AI safely and effectively today.

• Proven engineering leadership: managed engineers and managers, run programmes, owned budgets and supplier relationships.
• Owned an AI platform or enablement capability in a real organisation in the last 2-3 years, including deploying AI tooling company-wide, setting governance, and managing cost at scale.
• Built or significantly scaled a team.
• Current, hands-on fluency with the modern AI stack: frontier model platforms, agentic tooling and harnesses, MCP and integration patterns, evals, prompt and context engineering.
• AI security and governance experience: sandboxing, gateways (LLM, MCP, AI), DLP, guardrail enforcement, and data-boundary reasoning; able to form and defend risk positions.
• Cost engineering experience with usage-based pricing models, forecasting, and optimisation levers.
• Enough software and cloud architecture depth to work with Architects, Tech Ops and Decisioning, and to get hands-on with the platform in the early months.
• Core stack awareness: .NET on Azure and Python as the standard for AI, with polyglot pragmatism over allegiance to any single stack.
• Vendor and supplier evaluation and management experience in a fast-moving market.
• Evidence of business-case thinking: can express platform investment in ROI and EBITDA terms to a non-technical executive.
• Experience in financial services, another regulated industry, or a comparable risk-context environment is desirable.
• Hands-on experience selecting or implementing LLM/MCP gateways, sandboxed agent hosting, or agentic workflow orchestration platforms is desirable.
• Experience serving customer-facing AI models in production and partnering with data science teams on model safety and performance is desirable.