Description
• The AI & ML Engineering team accelerates the adoption of AI across the business, championing innovation while ensuring our machine learning solutions are robust, scalable, and cost-efficient
• We enable teams to solve problems using existing AI tools where possible and build custom solutions when needed. Our remit spans AI enablement, agentic systems development, and “conventional” ML engineering for non-GenAI applications e.g. recommender systems, forecasting models, and more
• We’re looking for an AI & ML Engineer who is passionate about building production-ready AI and machine learning solutions. You’ll work across a variety of AI initiatives, contributing to the design, implementation and deployment of scalable AI systems
• You will be expected to deliver high-quality AI/ML solutions across the full development lifecycle, from proof of concept through to production and ongoing optimisation
• Working closely with other engineers and stakeholders across the business, you’ll contribute to building reusable platforms, services and best practices while continuously developing your technical expertise
• This role reports to the Lead AI & ML Engineer and sits within the Data function, working as part of a specialist AI engineering team
• You’ll contribute to a mix of dedicated AI initiatives and larger cross-functional projects alongside colleagues from Technology, Data and Product
• The role covers the full AI/ML engineering lifecycle, from discovery to deployment and monitoring. Responsibilities include
• Designing and implementing agentic systems using techniques spanning RAG, grounding, prompt engineering, and orchestration on a GCP-first stack
• Building and maintaining production ML pipelines and services for non-GenAI use cases (e.g. recommender systems, customer segmentation models, marketing optimisation modules, leveraging supervised, unsupervised and/or econometric modelling approaches)
• Developing APIs and microservices for AI/ML solutions, ensuring security, scalability, and observability
• Implementing CI/CD for ML services, writing infrastructure as code, and monitoring for model/data drift and performance
• Establishing robust guardrails for safe AI usage, including prompt security, practical evaluation frameworks, and compliance with privacy regulations
• Contributing to reusable components, documentation and engineering best practices that improve AI/ML delivery across the organisation
• Collaborating with data engineers, data scientists, front & back-end engineers, product managers to deliver impactful solutions
• Supporting the evaluation of new AI technologies, frameworks and tooling, contributing ideas and recommendations for continuous improvement
• Who you will work with: Data and AI Team
Benefits
• Generous staff discount
• Generous product gifting
• Hybrid, flexible working
• Access to Tilbury Treats - discounts on everything from gym memberships to cinema tickets
• Dog friendly office on Monday and Fridays
• 25 days holiday plus bank holidays
• Other fabulous benefits such as life assurance, birthdays off work and pension contribution
• Bachelor’s or Master’s degree in Computer Science/Engineering/related field, or demonstrable relevant experience
• Previous experience developing agentic capabilities, such as agent skills, MCP servers, tool usage
• Strong Python engineering skills (FastAPI, testing, typing) and experience with cloud-native development (GCP preferred)
• Proven experience deploying and operating ML systems in production (batch and real-time)
• The role requires a blend of technical depth and product sense, including
• An interest in evaluating new AI technologies and contributing to technical discussions around build, buy or configure decisions
• Solid understanding of MLOps - CI/CD, IaC (Terraform), experiment tracking, model registry, and monitoring
• Strong grasp of security, privacy, and governance principles (IAM, secrets, PII handling)
• Effective communication skills and ability to work with both technical and non-technical stakeholders
• Familiarity with RAG architectures, prompt engineering, guardrails and evaluation techniques
• Hands-on experience with GCP Vertex AI (model endpoints, pipelines, embeddings, vector search) or equivalent cloud-native AI/ML platforms (e.g. AWS SageMaker, Azure ML) and agent orchestration frameworks (e.g. LangChain, LangGraph, ADK)
• A working understanding of cloud networking and platform infrastructure
• Experience with recommender systems and ranking models
• Knowledge of vector databases and retrieval strategies
• Familiarity with LLM evaluation tools (e.g., RAGAS, TruLens, LangSmith, Arize)
• Experience in e-commerce or retail environments
