Senior ML Engineer

Working at CreateFuture CreateFuture is an AI-native consulting partner where people do work that matters and are supported to do it well. We work alongside organisations such as PayPal, adidas, NatWest, FanDuel and Money Saving Expert, building digital products and services that make a difference while always putting people first. We’re a team of creators. We write code, shape delivery, build go-to-market strategies, develop AI solutions and create the practices that support our people. We work side by side with our clients, challenging what’s not working and helping them to build the future. Our commitment to craft, quality, and culture has helped us scale to over 600 people in just a few years. CreateFuture is a Great Place to Work-Certified™ company and has won Best Workplaces UK multiple years in a row. Join us on our journey. Let’s create tomorrow, together, today. About the role and team: We are looking for a Senior Data Engineer with a machine learning focus to join our data team, taking ownership of the data and feature pipelines that production ML systems depend on, training data preparation, feature engineering, and the path from a model that works in a notebook to one that runs, and keeps running, in production. You’ll work with a high degree of autonomy on the more complex and ambiguous problems across client engagements, partnering closely with data scientists and ML engineers, while mentoring junior engineers and helping to raise the bar on engineering practice. The foundations still matter: this is a data engineering role at heart, built on Python, PySpark, and platforms like Databricks, Snowflake, or AWS. What you'll be doing: • Take ownership of designing, building, and maintaining the production data and feature pipelines that machine learning systems depend on, training data preparation, feature engineering, and reproducible, point-in-time-correct datasets, with minimal supervision • Take models from prototype to production and keep them there: packaging and deployment alongside data science colleagues, model versioning, automated retraining, and the monitoring that catches drift and degradation before a client does • Build and maintain the batch and ELT pipelines the ML work sits on, using Python, PySpark, and cloud data services (e.g., AWS Glue, EMR, S3, Redshift, Athena, or the Databricks / Snowflake equivalents), making sound decisions on architecture, data modelling, and tooling and weighing up trade-offs in performance, cost, and maintainability • Own and continuously improve CI/CD for data and ML workflows, ensuring reliable, repeatable deployments of pipelines and models alike • Mentor and support junior and mid-level data engineers, reviewing code and sharing knowledge to raise the standard of delivery across the team • Troubleshoot and resolve complex issues in live data pipelines and deployed models, acting as a point of escalation for the wider team • Partner with data scientists, cross-functional teams, and client stakeholders to understand data and ML requirements and shape them into technical solutions We'd love to talk to you if you: • Have hands-on experience building the data and feature pipelines behind production machine learning, training data preparation, feature engineering, and feature stores, and understand why reproducibility and point-in-time correctness matter more than they first appear • Have worked with MLOps in practice rather than in principle: model versioning and registries, experiment tracking, automated retraining, and monitoring for drift and model degradation (MLflow, SageMaker, Databricks ML, Vertex AI or comparable) • Are comfortable with ML frameworks such as scikit-learn, PySpark MLlib, or PyTorch / TensorFlow, enough to productionise a data scientist