ВеликобританияHead of AI Platforms & Deployment
1 мес. назад
ВеликобританияHeadHybrid
ai platformsworkflow orchestrationmonitoringdeployment
Lead and build the AI Platforms and Deployment team to deliver AI strategy and deployment at Moneybox.
О компании
- 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 engineering 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, backed by a business case, and we are now building the team to deliver it. This is a newly created function and you will be its first hire. The Head of AI Platforms & Deployment is a player-manager role reporting to the Engineering Director, owning both sides of that strategy: the AI Platforms capability pillars (control, monitoring and sandboxing; agentic workflow orchestration; knowledge capture) and the AI Deployment team of forward-deployed engineers who put AI to work on real business problems. You will be hands-on from day one - enabling business deployment of AI with the tools we have today - whilst also building and delivering a business-driven, multi-year AI platforms strategy and hiring and managing the team that executes it. Why now: AI offers Moneybox significant opportunities, and unlocking them safely means managing the
Обязанности
- AI safety for internal use : sandboxing, policy, integrations, MCP governance, usage guardrails and monitoring expectations.
- The three platform pillars : control, monitoring and sandboxing (LLM and MCP gateway, anonymisation, local hardening, sandboxed cloud agent hosting); agentic workflow orchestration (a "software factory" in the cloud with hybrid self-serve and engineering-managed workflows); and knowledge capture and organisation.
- AI workflow tooling and the tiered graduation ladder from personal tool, to shared tool, to business system.
- Evolving existing deployments : tactical enhancement of what we run today so it aligns with the strategic direction.
Требования
- A proven engineering leader who has gone deep on AI. You have managed engineers and managers, run programmes, owned budgets and supplier relationships, and in the last two to three years you have owned an AI platform or enablement capability in a real organisation. Not a spectator or strategist-only.
- A player-manager. You are comfortable getting hands-on in the early months and equally comfortable stepping back as the team you have hired takes it on.
- Strategic and accountable. You build and deliver a 12-18 month strategy aligned to company objectives, make well-informed and timely decisions, and take full ownership of your function's performance and commitments.
- Unusually cross-functional. Every department is your stakeholder and customer. You will be recognised as the AI platforms expert at Moneybox, influencing exec and departmental leaders and resolving boundaries with Tech Ops and Decisioning through shared goals rather than turf.
- A team builder. You will build this team from scratch rather than inherit one, establishing a performance culture, developing your people, and running a team that itself models AI-enabled productivity.
- Commercially literate. You can express platform investment in ROI and EBITDA terms to a non-technical exec, and defend a risk position - on a connector approval, a token-versus-agent permission gap, a vendor - with clear reasoning.
Другое
- Proven engineering leadership: has managed engineers and managers, run programmes, owned budgets and supplier relationships.
- Has owned an AI platform or enablement capability in a real organisation in the last 2-3 years: deployed AI tooling company-wide, set governance, managed cost at scale.
- Has 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: sandboxing, gateways (LLM, MCP, AI), DLP, guardrail enforcement and data-boundary reasoning; able to form and defend risk positions.
- Cost engineering: usage-based pricing models, forecasting and optimisation levers.
- Enough software and cloud architecture depth to hold your own with Architects, Tech Ops and Decisioning, and to get hands-on with the platform in the early months. Our core stack is .NET on Azure and Python is the standard for AI, so we need polyglot pragmatism over allegiance to any single stack.
- Vendor and supplier evaluation and management in a fast-moving market.
- Evidence of business-case thinking: can express platform investment in ROI and EBITDA terms to a non-technical exec.
- Experience in financial services or another regulated industry, or a comparable risk-context environment.
- Hands-on experience selecting or implementing LLM/MCP gateways, sandboxed agent hosting or agentic workflow orchestration platforms.
- Experience serving customer-facing AI models in production and partnering with data science teams on model safety and performance.