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Farfetch is seeking an AI & Automation Engineer to partner with stakeholders across Catalogue, Content, Product and Data teams to identify and build automation and AI-driven solutions that solve real business problems.
In this role, you will own the full development lifecycle from problem framing and prototyping through production deployment, monitoring and optimization. You'll build robust GenAI applications and LLM-based workflows leveraging RAG, structured outputs, function calling, context management and multi-step reasoning. You'll construct and orchestrate reliable backend services and pipelines using Python, Apache Airflow, APIs and cloud-native solutions, optimizing for reliability, latency and inference costs.
You'll define and track operational metrics (throughput, error reduction, operational hours saved, metadata quality) to measure ROI of deployed solutions. You'll establish reusable patterns and best practices for building reliable, maintainable and scalable AI solutions, implementing AI safety best practices including prompt injection defense, guardrails and output validation. You'll explore and prototype emerging AI capabilities to solve real business problems and evolve them into production-ready solutions.
You'll help shape and expand the AI practice into a fully formed capability and service offering for the Catalogue and Content domain, combining systems design, consulting, delivery and innovation.
REQUIREMENTS:
Must have:
- 5+ years of relevant development experience (Software Engineering, MLOps, Applied AI, Automation or Data)
- Pragmatic approach to technology: knowing when to use AI, conventional automation or neither
- Deep expertise in Python, SQL and designing clean, maintainable backend services/microservices
- Hands-on experience with workflow orchestration platforms (e.g., Apache Airflow) and webhooks/API integrations
- Strong practical knowledge of prompt engineering, vector databases/embeddings, RAG architectures and output validation frameworks (e.g., Pydantic, Instructor, Guardrails)
- Familiarity with modern software delivery workflows: version control (Git), containerized applications (e.g., Docker) and basic cloud deployments
- Demonstrated experience with AI safety considerations: prompt injection defense, output validation and reliability engineering in production
- Ability to work across technical and business contexts and communicate effectively with non-technical stakeholders
Nice to have:
- Experience evaluating or building autonomous multi-agent frameworks (e.g., LangGraph, AutoGen, CrewAI)
- Domain knowledge in e-commerce content operations, product taxonomy management or catalog enrichment systems
- Background in machine learning, NLP or computer vision architectures
