Senior Engineering Lead (FinTech AI)

Description

The company is an online fashion retailer. This role leads the AI engineering team for its Finance, Commercial, Buying and Merchandising technology teams, which handle some of the highest-volume, most process-heavy work in the business.

You will manage engineers who build, deploy and support automation and agentic AI, and you will still write code when it matters.

The work covers accounts payable, reconciliation and expenses in Finance, and range planning, trading, stock and supplier data on the commercial side. The aim is to remove repetitive manual work, speed up decisions and give people time back.

This is a delivery role, not a strategy one. A central AI team sets the AI policy, framework, approved tooling and guardrails. You will build within them, stay aligned with that team and the AI centre of excellence, and own delivery from problem definition through to supporting live systems, including agents already running.

Responsibilities

• Line manage AI engineers, covering objectives, one-to-ones, development, career progression and performance.
• Hire, onboard and grow the team, building the skills needed as the work changes.
• Coach through design reviews, pairing and code review, giving direct feedback that helps people improve.
• Watch team health, balancing workload and capacity so people work on the right things.
• Set the team's technical direction, including architecture, reusable patterns and reference solutions.
• Set and enforce engineering standards: code quality, testing, evaluation, peer review, CI/CD and secure-by-default practice.
• Decide build or buy, and monitor running cost, performance and technical debt.
• Write code, prototype and take on the hardest builds yourself while unblocking the team.
• Design, build and ship production-ready agentic and automation solutions. Agents should plan, reason, use tools, retrieve information, act across systems and hand over to a person when needed.
• Build integrations to Microsoft Dynamics 365 Finance and Operations, ReconArt, Concur, ServiceNow and the data platform. Use retrieval augmented generation and grounding so answers match real business data.
• Plan and prioritise the backlog, manage capacity and dependencies, and move work quickly from proof of concept to production.
• Run the team's work in Azure DevOps and follow the wider delivery process, including governance, change control and release cadence.
• Take ownership of live agents and automations. Set up monitoring, alerting, runbooks and clear ownership, and lead incident response.
• Spend time with Finance, Commercial, Buying and Merchandising teams to learn how they work and what is worth automating. Explain options in plain language.
• Build within the central team's policies, tooling and guardrails, and act as the main contact with them and the AI centre of excellence.
• Measure and evidence benefits such as time saved, manual effort removed and shorter cycle times. Report progress and risks honestly to senior stakeholders.

Requirements

• Experience line managing engineers, including one-to-ones, development, performance and hiring, while staying hands on technically.
• A record of setting technical direction and engineering standards and raising the quality of a team's output.
• Experience owning delivery, including planning, prioritisation, capacity and reporting to senior stakeholders.
• Comfort working within a framework another team owns, alongside a central or platform team, and pushing back constructively when needed.
• Proven experience designing and building agentic AI that has gone live and is actually used, not just pilots.
• Practical experience with LLM and agent frameworks such as Microsoft Copilot Studio, Azure AI Foundry, Azure OpenAI, Semantic Kernel, LangChain, LangGraph, AutoGen or CrewAI.
• Working knowledge of core agent patterns: tool calling, orchestration, memory, multi-agent workflows and retrieval augmented generation.
• Experience with workflow automation tools such as Power Platform, Logic Apps or RPA.
• Experience evaluating agents on accuracy, guardrails, reliability and cost.
• A strong software or data engineering background, with solid coding in Python or C# or.NET, and experience building and integrating APIs and services.
• Experience running cloud solutions, ideally on Azure, with CI/CD, testing, monitoring and observability, and a security and data-conscious approach to sensitive financial data.
• Comfortable with non-technical stakeholders: running discovery, explaining technical ideas simply and influencing at senior level, focused on outcomes.
• Nice to have: experience with finance systems or processes such as accounts payable, reconciliation or financial control, or with Microsoft Dynamics 365 Finance and Operations.
• Nice to have: experience in retail, ecommerce or fashion, especially commercial, buying, merchandising or supply chain.
• Nice to have: data skills including SQL, data modelling, Databricks or Microsoft Fabric.
• Nice to have: experience with responsible AI and governance, Model Context Protocol (MCP), or ITIL-based service management.

Conditions

Works within the central AI team's policies, approved tooling and guardrails.

Adjustments can be requested during the hiring process.

Benefits

• Employee discount and access to employee sample sales.
• 25 days paid annual leave, plus an extra celebration day.
• Discretionary bonus scheme.
• Private medical care scheme.
• Flexible benefits allowance, which can be taken as extra cash or spent on other benefits.
• Personalised learning opportunities to help you thrive in your role.