3 hours ago
London, United KingdomSenior
Responsibilities
- Design and build shared AI platform capabilities on Azure, with emphasis on agent runtimes, orchestration, and tool integration.
- Contribute to the Agentic AI Platform initiative and define reusable patterns for building, integrating, and operating AI agents.
- Create standardized templates, reusable components, and reference implementations for LLM and generative AI workflows.
- Develop patterns for prompts, tool calling, multi-step agent flows, retries, failure handling, model access, routing, caching, token optimization, and cost controls.
- Implement secure and governed access to LLMs and enterprise tools using APIM, platform gateways, Entra ID, RBAC, managed identities, and MCP/tool abstractions.
- Contribute to LLMOps, AgentOps, and GenAIOps capabilities, including lifecycle management, evaluation, telemetry, run history, error analysis, and feedback loops.
- Build reliability and observability capabilities covering logging, metrics, traces, latency monitoring, alerting, scaling, and operational readiness.
- Apply software engineering practices to AI platform and agentic components, including automated testing and versioned deployments.
- Collaborate with Cloud Infrastructure, Security, Product, and engineering teams to establish standards and enable platform adoption.
Requirements
- Significant experience as an AI Engineer, AI Platform Engineer, Software Engineer, or similar delivering production-grade AI systems.
- Hands-on production experience with LLMs, generative AI, and agent-based systems.
- Experience building reusable AI capabilities, platforms, frameworks, templates, or services for adoption by other engineering teams.
- Strong understanding of the end-to-end AI lifecycle from experimentation through deployment and operation.
- Practical knowledge of production LLM and generative AI runtime concerns, including model access, routing, caching, token usage, cost optimization, and reliability.
- High proficiency in Python and experience building APIs and service-oriented systems.
- Experience with automated testing, versioned deployments, observability tooling, and telemetry-driven reliability improvements.
- Hands-on experience with Azure cloud and AI services.
- Experience with Azure AI Foundry is preferred, while comparable generative AI or agent platforms are also considered.
- Experience with Azure API Management, particularly as a governance or integration boundary, is preferred.
- Familiarity with AgentOps, MLOps, or GenAIOps concepts, including monitoring, evaluation, and feedback loops.
- Experience establishing engineering standards, reusable patterns, or best practices for AI development.
- Strong collaboration and influencing skills, with a pragmatic engineering-led approach to responsible and ethical AI.
Benefits
- Employee discount and access to 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 can be taken as cash or used toward other benefits.
- Personalized learning and in-the-moment development opportunities.
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.