AI Engineer, Director

Join us as a Data Engineer-AI

Build and lead a team that ships production generative and agentic AI systems used by millions of customers and colleagues — solving problems that don't yet have a playbook.
Combine strong people leadership with deep, hands-on technical expertise, staying close to the detail while building a high-performing, engaged, and continuously improving team.
Have the autonomy to choose the right tools, frontier models, and architectures for the job, and the scale to see your work make a real-world impact
We are offering this role at director level

What you’ll do

In this role, you’ll develop and share knowledge of business data structures and metrics, advocating for changes when needed for product development. You’ll also educate and embed new data techniques into the business through role modelling, training, and experiment design oversight.

We’ll look to you to drive DevOps adoption into the delivery of data engineering, proactively performing root cause analysis while resolving issues. You’ll also deliver a clear understanding of data platform cost levers to meet department cost savings and income targets.

You’ll also be responsible for:

Lead and line-manage a team of AI engineers: setting objectives, managing performance, coaching and developing careers, and building an inclusive, high-trust culture
Own the technical vision and roadmap for the team's AI systems, and make the architectural decisions that shape how we build, evaluate, and safely operate LLM-powered applications
Stay hands-on, contributing to design, code, and reviews, and setting the bar for engineering quality across multi-agent workflows, Retrieval-Augmented Generation (RAG) pipelines, and LLM integrations
Design agent-to-agent communication frameworks, including structured messaging, shared state, coordination protocols, and failure handling.
Architect and optimise RAG pipelines, covering document chunking, embedding generation, vector storage, retrieval evaluation, ranking, and freshness handling
Design and own the data pipelines that feed AI systems — ingestion, transformation, and feature/embedding preparation — built for reliability, data quality, and lineage
Build and operate orchestrated, scheduled workflows using tools such as Apache Airflow, with monitoring, retries, and clear failure handling
Leverage cloud data platforms such as Snowflake (alongside AWS data services) for scalable storage, transformation, and analytics that underpin AI and ML workloads
Drive optimisation for low latency, reliability, throughput, and cost across production AI workloads on AWS
Integrate and orchestrate a range of frontier LLM providers, balancing capability, cost, latency, and risk, and designing for portability across models and providers

The skills you’ll need

We’re looking for someone with strong communication skills and the ability to proactively engage and manage a wide range of stakeholders. You’ll have extensive experience working in a governed, and regulatory environment.

You’ll also need:

At least sixteen years of experience in IT industry with minimum five years experience in technical leadership
Strong experience designing and shipping production AI/ML or generative AI systems at scale
Proven proficiency in Python, PySpark, SQL, CICD pipelines, Git version control
Advanced experience of integrating frontier LLM providers such as OpenAI, Anthropic and agent frameworks such as LangGraph or LangChain
Experience of designing robust data pipelines, with hands-on experience of workflow orchestration tools such as Apache Airflow
Proven experience building and operating cloud-native AI services on AWS such as Amazon Bedrock, SageMaker, ECS/EKS, Lambda, using Docker, Kubernetes, and infrastructure as code

Hours

Job Posting Closing Date:

25/06/2026