Senior Machine Learning Engineer in London

Requirements

• Production ML Experience: Proven experience deploying Machine Learning models into high-traffic production environments (retail, fintech, or utilities experience is a plus).
• Tech Stack: Strong proficiency in Python and software engineering best practices (unit testing, modular code, Git). Experience with containerization (Docker, Kubernetes) is essential.
• MLOps Tooling: Experience with model registries and monitoring tools (e.g., MLflow, Grafana).
• Desirables: Experience with Feature Stores (e.g., Feast, Tecton). Knowledge of streaming data technologies (Kafka, Pyspark). Hands-on experience building or deploying LLM-based applications, specifically working with RAG architectures and vector databases.
• You have a track record of leading high-impact initiatives that align with company strategy.
• You can evaluate proposed work against team goals and provide critical feedback to ensure value delivery.
• You are capable of independently implementing small to medium sized features through to completion.
• Continuous improvement mindset: Identify process gaps and proactively propose solutions, seeking out feedback from your team.
• Experience in working in a relevant consumer-centric domain.
• Can advise stakeholders on how Machine Learning Engineering can be applied to solve business problems.
• Collaboration: A "Software Engineering mindset" with the ability to work empathetically with Data Scientists, understanding their workflows while enforcing production standards.
• Strategic Problem Solving: Ability to break down vague, high-level business requirements into concrete, scalable technical architectures.
• Clear Communication: Excellent verbal and written skills, with the ability to influence technical and non-technical audiences.
• Accountability: Willingness to take ownership of critical systems and participate in on-call rotations.
• Continuous Learning: Proactively seeking out the latest industry trends and introducing relevant innovations to the team.

Don’t worry if you don’t have the whole list. If you feel you have most of it and can learn the rest pretty quickly then please don’t hesitate to apply. Overall we are looking for imaginative and pragmatic problem-solvers who want to help make a positive impact with data at UW.

What the job involves

• We work in small, fully autonomous teams that have real ownership of their products. We use the best tool for the job and constantly look for better.
• We are seeking a production-focused Machine Learning Engineer to bridge the gap between data science research and scalable, reliable software.
• In this role, you will partner with Data Scientists to re-architect experimental models (POCs)—such as Next Best Action and Churn Propensity—for production.
• You will own "Day 2" operations including deployment, latency optimization, and monitoring, while also building the infrastructure for GenAI and RAG applications powering our tools.
• As a Machine Learning Engineer at UW, your responsibilities will include:
• Predictive Modelling: Design and deploy robust ML models to solve business challenges, specifically Churn Propensity and Next Best Action (NBA) engines.
• Customer Analytics: Develop advanced Customer Segmentation using clustering techniques to tailor services and communications.
• Commercial Valuation: Own xLTV and ROI logic, modeling long-term customer value to optimize acquisition and retention spend.
• Deployment & Ops: Collaborate with Data Engineers to productionise scalable models, ensuring continuous monitoring for drift and performance.
• Experimentation: Design and analyse A/B tests to validate model effectiveness and measure commercial uplift.
• Stakeholder Partnership: Translate complex statistical outputs into actionable insights for Marketing, Product, Commercial and Ops stakeholders.