Senior Engineering Manager (AI)

Experian is a global data and technology company, powering opportunities for people and businesses around the world. We help to redefine lending practices, uncover and prevent fraud, simplify healthcare, create marketing solutions, and gain deeper insights into the automotive market, all using our unique combination of data, analytics and software. We also assist millions of people to realize their financial goals and help them save time and money.

We operate across a range of markets, from financial services to healthcare, automotive, agribusiness, insurance, and many more industry segments.

We invest in people and new advanced technologies to unlock the power of data. As a FTSE 100 Index company listed on the London Stock Exchange (EXPN), we have a team of 22,500 people across 32 countries. Our corporate headquarters are in Dublin, Ireland. Learn more at experianplc.com.

Job description

We are looking for a collaborative and customer focused Senior Engineering Manager in AI to work within the ECS Technology function; a technically strong engineering leader who manages people effectively while staying deeply engaged in architectural decision‑making, AI systems engineering, platform strategy and AI‑driven software engineering practices. This is a hands‑on technical leadership role: you will set engineering direction, define platform approaches and ensure the scalability and maintainability of LLM‑based conversational experiences.

Who we are

Experian Consumer Services (ECS) are the team behind your Experian Free Credit Score, Experian Boost and the advanced Credit Expert product. We are an agile business, that has been through a complete technical and organisational transformation.

Why this role is important to us

The role is platform‑minded. The teams you lead will build and operate foundational capabilities, patterns and tooling that enable other product‑engineering teams to contribute features into AI surfaces - from our on-property chatbot EVA to off-property integrations such as OpenAI Apps and beyond. Success is measured not just by what your own teams deliver directly, but by how effectively other teams can build on the platform you provide, and by the breadth of functions across the business that benefit from the reusable agentic capabilities your teams create.

You will work closely on Model Context Protocol design and implementation - owning the design and creation of MCP servers that power on‑ and off‑property experiences - and partner with Cloud Services (internal IaC) and AWS to ensure secure, scalable deployment models for conversational AI systems.

An explicit part of this role includes driving AI‑driven software engineering across teams: identifying opportunities to use LLMs and automation to accelerate development workflows, improve engineering throughput, reduce toil, and strengthen overall engineering effectiveness.

Key Responsibilities

• Lead engineering teams building LLM‑based conversational capabilities and platform components that enable contribution from multiple domains.

• Design, build and operate MCP servers (Model Context Protocol) to provide consistent, secure and extensible interfaces into on‑ and off‑property conversational channels.

• Define and evolve platform patterns, abstractions, SDKs and extension points that let other teams build into conversational channels with minimal friction.

• Drive adoption of AI‑driven software engineering practices, including AI‑assisted coding, code review, test generation, operational automation and developer‑tooling enhancements.

• Partner with Cloud Services (IaC) and AWS to provide well‑governed, scalable infrastructure and deployment tooling.

• Establish architectural standards that maximise extensibility and long‑term maintainability while avoiding premature complexity.

• Implement robust LLM‑specific testing and evaluation (regression harnesses, behaviour change detection, safety checks, latency and quality monitoring).

• Drive engineering practices for LLM systems: retrieval‑augmented generation, prompt lifecycle management, model/provider integration, and platform‑level observability.

• Enable domain teams via documentation, tooling, examples, training and support; track adoption, time‑to‑first‑feature and developer experience as key metrics.

• Provide technical coaching and clear expectations for engineering quality across teams.

• Ensure security, reliability and compliance across conversational systems and platfo…