Senior AI / LLM Engineer

We'reASOS, the online retailer for fashion lovers all around the world.

We exist to give our customers the confidence to be whoever they want to be, and that goes for our people too. At ASOS,you'refree to be your true self without judgement, and channel your creativity into a platform used by millions.

Everyone needs some help showing up as their best self.We'reDisability Confident Committed - let our Talent team know if you need any reasonable adjustments throughout the recruitment process

Job Description

We’rehiring aSenior AI / LLM Engineerto design and industrialise the core AI capabilities that will power ASOS.

At ASOS, AI is central to how we evolve the customer experience — from product discovery and personalisation through to operational decision-making.You’llwork on systems thatoperateat real scale, supporting experiences used by millions.

This role sits at the centre of our AI differentiation — focused on solvingdeep technical problems(models, reasoning systems, orchestration), not just feature delivery.You’llcreatereusable, production-grade AI platformsthat teams across ASOS canleverage, accelerating how we build and scale intelligent products.

We’relooking for someone whothinks in systems, not features— with strong abstraction capability and a mindset of building once and reusing at scale.You’llbe comfortable operating in ambiguity, working on frontier AI problems, and balancing innovation with the realities of production environments.

Ifyou’remotivated by solving complex challenges and seeing your work adopted across a global platform, this is a high-impact opportunity.

Responsibilities

• Design and buildLLM-powered systems(RAG, fine-tuning, tool use, multi-agent orchestration)
• Developagentic workflowsfor automation, reasoning, and conversational experiences
• Define patterns forautonomous + human-in-the-loop systems
• Build and scale solutions on theAzure AI stack(Azure OpenAI, AI Studio, Azure ML, Cognitive Services)
• Create reusable infrastructure:Prompt orchestration layers,Vector search and retrieval pipelines &Evaluation and observability frameworks
• Design and implementLLM evaluation frameworks(offline + online)
• ImplementAI safety guardrails(hallucination control, filtering, explainability)
• Partner with Trust & Security to embedAI risk controls by design
• Enablehorizontal reuse across squads— building once, scaling many times
• Optimise systems forperformance, cost, and reliability(token-aware design)
• Champion best practice inAI engineering, governance, and platform thinking
• Support a culture ofinclusive, responsible AI development

Qualifications

• Strong hands-on experience with theAzure AI ecosystem— including Azure OpenAI, Azure AI Studio, and Azure Machine Learning
• Experience building and runningproduction-grade AI systemson cloud infrastructure (APIs, event-driven architectures, scalable compute such as AKS or similar)
• ProficiencyinPython, with experience working across modern AI frameworks (e.g. Semantic Kernel,LangChainor equivalents)

LLM systems and architecture:

• Deep understanding ofLLMs and transformer-based models, including embeddings, tokenisation, and context management
• Experience designing and optimisingLLM-powered systems, including fine-tuning approaches, structured prompting, and model selection trade-offs
• Experience buildingend-to-end RAG pipelines, including retrieval strategies, vector search, and grounding techniques

Agentic systems & orchestration:

• Experience designingagent-based systems, including multi-agent patterns, tool usage, and workflow orchestration
• Understanding ofstate, memory, and event-driven pipelinesin conversational or decisioning systems

Quality, safety & production readiness:

• Experience implementingevaluation frameworksto measure model quality and performance
• Strong understanding ofAI safety, governance, and guardrails(hallucination mitigation, content safety, explainability)
• Experience designingsecure, scalable, and cost-efficient systemsin production environments

Nice to have:

• LLMOps/MLOpsexperience (CI/CD, experiment tracking, observability)
• Performance and cost optimisation (caching, batching, model routing)

Additional Information

• Employee sample sales
• 25 days paid annual leave + an extra celebration day for a special moment
• Discretionary bonus scheme
• Private medical care scheme
• Flexible benefits allowance - which you can choose to take as extra cash, or use towards other benefits
• Opportunity for personalised learning and in-the-moment experiences that enable you to thrive and excel in your role

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