Machine learning engineer in AI systems

Compare the Market is building AI systems that connect directly to how millions of people in the UK find and buy financial products - and as a Machine Learning Engineer, you will play an important role in making those systems production-ready. That means contributing to the engineering that takes ML models and AI capabilities from experimentation into reliable, scalable production use: the pipelines, the deployment tooling, the monitoring, and the shared components that make the whole thing work.

Responsibilities:

• Deliver well-scoped ML and AI system components end-to-end, from design through to testing, deployment, and production support.
• Contribute hands-on code to model packaging, deployment, and lifecycle automation.
• Build and improve systems that monitor model performance, drift, reliability, and operational health.
• Support both batch and real-time ML workloads depending on use case requirements.
• Work on emerging AI and LLM-powered capabilities, helping integrate modern AI techniques into production systems where they can deliver real user value.
• Contribute to the evolution of our internal ML and AI platform to support experimentation, governance, and collaboration.
• Apply and help promote best practices for ML and AI system design, including reproducibility, testing, CI/CD, model and agent observability and evaluation.
• Contribute to shared tools and libraries that accelerate safe, efficient, and scalable ML development.
• Work closely with data scientists to help productionise experimental models and turn prototypes into robust services.
• Contribute to code reviews, providing and receiving constructive feedback.
• Participate in architecture and design discussions, raising considerations around scalability, reliability, and system interactions.
• Contribute to a culture of engineering excellence, collaboration, and continuous learning.
• Stay curious about emerging tools and approaches in MLOps and applied AI, and bring ideas for improvement to the team.
• Support responsible AI practices, contributing to explainability, auditability, and fairness in ML systems.

Requirements:

• Some experience delivering ML or AI components in a production or near-production environment.
• Solid software engineering skills in Python, with an understanding of how to write maintainable, testable code.
• Familiarity with ML tooling and platforms such as Databricks, MLflow, Airflow, or equivalent.
• Familiarity with CI/CD pipelines and how they apply to ML systems.
• An understanding of ML lifecycle challenges - versioning, testing, monitoring, governance.
• Good collaboration and communication skills, with an ability to work effectively across data science, engineering, and product teams.

Desirable:

• Exposure to LLM-based systems - for example prompt engineering, RAG, or orchestration frameworks such as LangGraph or LangChain.
• Familiarity with multi-step AI patterns - systems where models plan, retrieve information, and take sequences of actions.
• Active personal use of AI-assisted or agentic coding tools, and an interest in how similar patterns could be applied to automate and accelerate ML engineering workflows.
• Experience or interest in financial services, insurance, or another regulated sector.
• Interest in responsible AI and ML model governance.

Here, you’ll be encouraged to think differently, act boldly, and deliver brilliantly in a culture that values results and rewards progress. We believe diverse teams make better decisions, and we’re committed to creating an inclusive workplace where everyone feels empowered to grow, contribute, and thrive.