We’re looking for a Senior Engineering Lead to build and lead our AI engineering team in FinTech and Fashion Tech at ASOS, the technology teams behind Finance and our Commercial, Buying and Merchandising functions. This is a hands on engineering leadership role. You’ll line manage and grow a team of AI Engineers who build, deploy and support automation and agentic AI solutions, and you’ll still write code yourself when it counts.
The teams we support run some of the highest volume, most process heavy operations in the business, from accounts payable, reconciliation and expenses in Finance, through to range planning, trading, stock and supplier data on the commercial side. There is real scope to take repetitive manual work off people, speed up decisions and give them their time back.
This is a delivery role rather than a strategy one. ASOS has a central AI team who own our AI policy, framework, approved tooling and guardrails, and you won’t be setting that direction for the wider business. You’ll build for the teams we support within it, working closely with the central team and our AI centre of excellence to stay aligned. Within that remit you own delivery end to end, from understanding the problem through to supporting what goes live, including the agents already running today.
Key Responsibilities
Leading and Growing the Engineering Team
- Line manage a team of AI Engineers, covering objectives, one to ones, development, career progression and performance.
- Grow the team. Hire well, onboard new engineers properly, and build the skills we need as the work changes.
- Coach and mentor through design reviews, pairing and code review, and give honest feedback that helps people improve.
- Look after the health of the team, balancing workload and capacity so people are on the right work and not carrying too much.
Technical Direction and Engineering Standards
- Own the technical direction for the team, including the architecture, reusable patterns and reference solutions we build from.
- Set and hold the engineering standards, covering code quality, testing, evaluation, peer review, CI/CD and secure by default habits.
- Make the build or buy calls for your team, and keep an eye on running cost, performance and technical debt.
Building and Running AI Solutions
- Stay hands on. Lead from the front by writing code, prototyping, unblocking your team and picking up the hardest builds yourself.
- Design, build and ship production ready agentic AI and automation solutions: agents that can plan, reason, use tools, retrieve information, act across our systems and hand back to a person when they should.
- Build the integrations that make agents genuinely useful, connecting them to platforms such as Microsoft Dynamics 365 Finance and Operations, ReconArt, Concur, ServiceNow and our data platform, and use retrieval augmented generation and grounding so solutions are accurate against real business data.
Owning Delivery
- Own delivery for the team. Plan and prioritise the backlog, manage capacity and dependencies, and move work quickly from proof of concept into production.
- Run the team’s work through Azure DevOps and follow the wider ASOS delivery process, including governance, change control and release cadence.
- Own the agents and automations already live, put proper operational foundations in place including monitoring, alerting, runbooks and clear ownership, and lead incident response when things go wrong.
Working with Stakeholders and the Central AI Team
- Get out into Finance, Commercial, Buying and Merchandising to understand how people really work and what is genuinely worth automating, and explain the options back in plain language.
- Build inside the policies, approved tooling and guardrails the central AI team sets, and be our main point of contact with them and the AI centre of excellence so we stay aligned.
- Track and evidence the benefit delivered, whether that is time saved, manual effort removed or cycle times cut, and report progress and risks honestly to senior stakeholders.
Why This Role Matters
Automation and agentic AI are the biggest opportunity we have to change how these teams work. Done well, they take repetitive work off people’s plates, shorten month end close and trading decisions, and free our teams up for the things only people can do.
The central team provides the framework and the guardrails. What we need now is someone to build and lead the team that delivers against it: a hands on engineering leader who can grow engineers, set the technical bar, keep stakeholders confident, and still open the laptop and build.