14 hours ago
London, United KingdomSenior
Responsibilities
- Design and build LLM-powered systems using RAG, fine-tuning, tool use, and multi-agent orchestration.
- Develop agentic workflows for automation, reasoning, and conversational experiences.
- Define autonomous and human-in-the-loop system patterns.
- Build and scale production solutions on Azure AI, including reusable prompt orchestration, retrieval, evaluation, and observability infrastructure.
- Design and implement offline and online LLM evaluation frameworks.
- Implement AI safety guardrails for hallucination control, filtering, explainability, and content safety.
- Partner with Trust & Security to embed AI risk controls by design.
- Create reusable AI capabilities such as stylist reasoning, product understanding, and copilots.
- Enable horizontal reuse across squads by building capabilities once and scaling them broadly.
- Optimize systems for performance, cost, reliability, and token efficiency.
- Champion AI engineering, governance, platform-thinking, and responsible AI best practices.
Requirements
- Strong hands-on experience with the Azure AI ecosystem, including Azure OpenAI, Azure AI Studio, and Azure Machine Learning.
- Experience building and operating production-grade AI systems on cloud infrastructure, including APIs, event-driven architectures, and scalable compute such as AKS or similar.
- Proficiency in Python and experience with modern AI frameworks such as Semantic Kernel, LangChain, or equivalents.
- Deep understanding of LLMs and transformer-based models, including embeddings, tokenization, and context management.
- Experience designing and optimizing LLM-powered systems, including fine-tuning, structured prompting, and model-selection trade-offs.
- Experience building end-to-end RAG pipelines with retrieval strategies, vector search, and grounding techniques.
- Experience designing agent-based systems with multi-agent patterns, tool usage, and workflow orchestration.
- Understanding of state, memory, and event-driven pipelines in conversational or decisioning systems.
- Experience implementing evaluation frameworks to measure model quality and performance.
- Strong understanding of AI safety, governance, and guardrails, including hallucination mitigation, content safety, and explainability.
- Experience designing secure, scalable, and cost-efficient production systems.
- Experience with LLMOps or MLOps, CI/CD, experiment tracking, and observability is preferred.
- Experience with performance and cost optimization techniques such as caching, batching, and model routing is preferred.
Benefits
- Employee discount and employee sample sales.
- 25 days of paid annual leave plus an additional celebration day.
- Discretionary bonus scheme.
- Private medical care scheme.
- Flexible benefits allowance that may be taken as extra cash or used toward other benefits.
- Personalized learning and in-the-moment development experiences.
- ASOS supports reasonable adjustments throughout the recruitment process.
Categories
About ASOS
ASOS is a global online fashion and beauty retailer for 20‑somethings, selling own‑brand and third‑party labels via its website and mobile apps. The company earns revenue from direct‑to‑consumer e‑commerce, shipping to customers worldwide and offering marketplace services for brands. Founded in 2000 and headquartered in London, ASOS is a public company listed on the London Stock Exchange and runs in‑house content and fulfillment operations at scale.