Machine Learning Engineer | Permanent | Hybrid working in London | Beverage Trading Platform

The Role

Seeking an experienced Machine Learning Engineer to build the technical foundation for an AI-driven platform. While Data Scientists focus on model design, your mission is to productionise, scale, and serve these models with high availability and low latency. You will own the MLOps infrastructure on Databricks and AWS.

What You’ll Do

• Productionise ML Pipelines: Engineer robust, scalable data and ML pipelines using PySpark on Databricks.
• Implement MLOps: Design and maintain CI/CD workflows using MLflow and Databricks Asset Bundles.
• Model Serving: Deploy models to production using Mosaic AI Model Serving or similar serverless endpoints.
• Infrastructure: Manage data and ML infrastructure on AWS (S3, Lambda) and Databricks Unity Catalog.
• Monitoring: Implement comprehensive monitoring for model drift and data quality to ensure 99.9% availability.

What We’re Looking For

• 5+ years in Data Engineering or Machine Learning Engineering.
• Expert Python skills: Production-grade programming including typing, testing, and modular design.
• Databricks Ecosystem: Deep proficiency with PySpark, Delta Lake, and Unity Catalog.
• Cloud Native (AWS): Strong experience with AWS core services and Infrastructure-as-Code principles.
• MLOps Tools: Hands-on experience with MLflow, Docker, and CI/CD tools like GitHub Actions.
• Vector Search: Familiarity with scaling vector databases such as Pinecone or Weaviate