machine learning engineer for financial products

Compare the Market helps millions of people in the UK find and buy financial products, making financial decision-making easier. The business builds AI systems for financial product discovery and purchasing.

• Own the end-to-end delivery of production machine learning and AI solutions with data scientists and product teams
• Design and build model pipelines for training, validation, and deployment
• Write code for model packaging, deployment, and lifecycle automation
• Build systems to monitor model performance, drift, reliability, and operational health in real time
• Support batch and real-time ML workloads
• Integrate emerging AI and LLM-powered capabilities into production systems where they can deliver user value
• Evolve the internal ML and AI platform to support experimentation, governance, and collaboration
• Define and promote best practices for ML and AI system design, including reproducibility, testing, CI/CD, observability, and evaluation
• Develop shared tools and libraries for safe, efficient, and scalable ML development
• Work with data scientists to productionise experimental models and turn prototypes into robust services
• Mentor and review code for other engineers and contributors
• Provide technical leadership across ML and AI initiatives and contribute to architecture discussions and design reviews
• Contribute to a culture of engineering excellence, collaboration, and continuous learning
• Evaluate and adopt emerging MLOps and applied AI tools and approaches where appropriate
• Support responsible AI practices, including explainability, auditability, and fairness in ML systems

• Hands-on experience with LLM-based systems, including prompt engineering, RAG, tool use, or orchestration frameworks such as LangGraph or LangChain
• Familiarity with multi-step AI patterns involving planning, information retrieval, and sequences of actions
• Strong experience deploying ML models to production in cloud-native environments
• Strong Python software engineering skills, including scalable services, APIs, and production-quality code
• Experience with modern ML tools and platforms such as Databricks, MLflow, Airflow, Kubeflow, SageMaker, or Vertex AI
• Familiarity with CI/CD pipelines and infrastructure as code, such as Terraform or CloudFormation
• Experience building robust, maintainable, and testable ML pipelines and APIs for batch or real-time model delivery
• Strong understanding of ML lifecycle challenges, including versioning, testing, monitoring, and governance
• Excellent collaboration and communication skills, with experience working across data science, engineering, and product teams
• Будет плюсом: personal use of AI-assisted or agentic coding tools and interest in applying similar patterns to ML engineering workflows, experience in financial services, insurance, or another regulated sector, experience deploying real-time or streaming ML models, passion for automation, tooling, and reusable systems, interest in responsible AI and ML model governance

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