Machine Learning Engineer

At Compare the Market, we're applying AI to real-world problems that help millions of people make smarter financial decisions. As a Machine Learning Engineer, you'll work at the heart of this transformation-building the infrastructure and tooling that enables our data scientists to move from prototype to production quickly, safely, and at scale.

You'll be part of a growing ML Engineering team, contributing to a modern MLOps platform and delivering robust ML services in collaboration with product, engineering, and data science colleagues. This is a hands-on role that's ideal for someone who wants to grow in a high-impact environment with strong mentorship and real ownership.

What you'll be doing

ML Engineering & Deployment

• Develop and maintain machine learning pipelines for training, validation, and deployment
• Collaborate with data scientists to productionise models and turn prototypes into performant, reliable services
• Contribute to deployment tooling and automation for both batch and real-time ML use cases
• Build monitoring and alerting for model health, performance, and data drift

Platform & Standards

• Support the evolution of our internal ML platform and development workflows
• Apply best practices in testing, CI/CD, version control, and infrastructure-as-code
• Contribute to team libraries, reusable components, and shared deployment patterns

Collaboration & Growth

• Work in cross-functional teams alongside product managers, engineers, and analysts
• Participate in design sessions, peer reviews, and sprint planning
• Learn from and be mentored by experienced ML Engineers and technical leaders

Do you have experience in Python?, Must Have

• Practical experience deploying ML models into production environments
• Strong Python development skills and understanding of ML model structures
• Familiarity with tools such as MLflow, Airflow, SageMaker, or Vertex AI
• Understanding of CI/CD concepts and basic infrastructure automation
• Ability to write well-tested, maintainable, and modular code
• Strong collaboration skills and a growth mindset
• A background in software engineering, computer science, or a quantitative field-or equivalent hands-on experience in ML delivery

Nice to Have

• Experience working in regulated sectors such as insurance, banking, or financial services
• Exposure to Databricks, container orchestration (e.g. Kubernetes), or workflow engines (e.g. Argo, Airflow)
• Familiarity with real-time model deployment, streaming data, or event-driven systems (e.g. Kafka, Flink)
• Interest in MLOps, model governance, and responsible AI practices
• Understanding of basic model evaluation, drift detection, and monitoring techniques