INTRODUCTION

At Burberry, we believe creativity opens spaces. Our purpose is to unlock the power of imagination to push boundaries and open new possibilities for our people, our customers and our communities. This is the core belief that has guided Burberry since it was founded in 1856 and is central to how we operate as a company today.  

We aim to provide an environment for creative minds from different backgrounds to thrive, bringing a wide range of skills and experiences to everything we do. As a purposeful, values-driven brand, we are committed to being a force for good in the world as well, creating the next generation of sustainable luxury for customers, driving industry change and championing our communities.

JOB PURPOSE

The Senior Developer, AI & Emerging Tech is accountable for evolving the engineering and architecture of Burberry’s shared agentic AI platform, including the orchestration layer, observability, agent registry, prompt engineering patterns, model selection and evaluation frameworks that underpin Burberry’s enterprise agentic AI ambitions. Sitting within the centralised Architecture function under the Director, EA, the role will help shape the agentic AI platform strategy across IT and ensure the platform is scalable, secure, well-governed and aligned to business and technology priorities. As a new capability for Burberry, the role carries a foundational responsibility to establish the engineering practices, tooling, documentation and knowledge base required to build and sustain this capability from the ground up.

RESPONSIBILITIES

• Design, build and maintain the shared agentic platform services that domain-aligned and platform teams consume to deploy agents at enterprise scale.

• Own the agent platform engineering layer, ensuring agents are orchestrated, registered, monitored and governed to enterprise standards.

• Develop and maintain prompt engineering patterns, model selection frameworks, operations and evaluation methodologies.

• Serve as the interim centre of agentic development expertise; progressively transfer knowledge and patterns to domain teams

• Collaborate with Enterprise and Solution Architects to define agentic orchestration strategy and platform requirements and ensure platform engineering aligns with the enterprise reference architecture, design patterns and technology standards.

• Work with integration engineers to ensure seamless agent-to-system connectivity, supporting integration standards and patterns for agents and enabling agents to consume enterprise tools, systems and services through the governed integration layer.

• Work with data platform engineers to ensure agents can consume shared data infrastructure, including knowledge bases, real-time data feeds, data lineage and governed metrics, in a way that is reliable, performant, secure and aligned to data governance and AI ethics standards.

• Support domain-aligned product teams and Enterprise IT platform teams in the build and deployment of domain-specific and platform-specific agents, providing platform services, engineering guidance and pattern compliance review without owning the agents themselves.

• Establish and maintain clear documentation of agentic platform architecture, orchestration logic, agent registry schemas, evaluation frameworks and engineering standards.

• Define and enforce platform SLOs (service-level objectives) for the agentic layer, including orchestration latency, agent reliability and evaluation pipeline throughput. 

• Continuously evaluate emerging agentic AI tooling, frameworks and architectural patterns; recommend adoption and conduct proof-of-concept validation where justified

EXPERIENCE

• 5+ years' experience in AI/ML engineering, platform engineering or software engineering roles. • Hands-on experience designing, building and operating agentic AI systems, LLM-based platforms or multi agent orchestration frameworks on Azure, AWS or GCP (or similar).
• Core technical skills should include Python and modern software engineering practices; experience with LLM and agent development frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen or CrewAI; practical knowledge of RAG patterns, vector databases and embeddings; API design and integration using REST, event-driven and workflow-based patterns; CI/CD, automated testing and infrastructure-as-code; containerisation and deployment using Docker and Kubernetes; and hands-on experience with cloud AI services, preferably Microsoft Azure AI Foundry, Azure OpenAI, Microsoft Copilot Studio or equivalent services on AWS or GCP.
• Operational experience should include building production-grade services with monitoring, alerting, logging, incident management, release management, rollback procedures and service ownership disciplines, with the ability to define and operate SLOs for platform capabilities used by multiple teams.
• Strong understanding of enterprise integration patterns and how agents interact with systems, APIs and data sources.
• Practical familiarity with AI governance, risk and security controls, including model and prompt versioning, auditability, human-in-the-loop review, responsible AI guardrails, agent identity and access control, secrets management, data classification, data loss prevention, policy enforcement, bias monitoring and evaluation evidence suitable for internal assurance review.
• Thrives in ambiguous, greenfield environments where engineering practices and standards must be established from scratch; balances pace of delivery with sustainable, well-documented foundations. 
• Proven ability to influence and collaborate with senior technical stakeholders (architecture, security, data, delivery) across a matrixed enterprise organisation.
• Familiarity with AI evaluation methodologies: automated testing of agent outputs, hallucination detection, regression testing for prompt changes, and human-in-the-loop feedback loops.
• Detail-oriented with a commitment to documentation, knowledge sharing and engineering consistency. 
• Experience in Retail, Luxury or Consumer goods industries is beneficial.