Senior Machine Learning Engineer

ML Engineering & AI Systems:

• Own the end-to-end delivery of production machine learning and AI solutions in collaboration with data scientists and product teams
• Design and build model pipelines for training, validation, and deployment using modern tooling (e.g. MLflow, Kubernetes)
• Contribute hands-on code to model packaging, deployment, and lifecycle automation
• Build systems that monitor model performance, drift, reliability and operational health in real time
• 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

Why this role matters: At Compare the Market, we’re scaling our AI capabilities to power intelligent, personalised experiences that help millions make smarter financial decisions. As a Senior Machine Learning Engineer, you’ll play a critical role in enabling the deployment, monitoring, and scaling of production-grade ML and AI systems—making sure that our AI ambitions are not only possible, but production-ready. This role blends hands-on engineering with architectural design, experimentation support, and MLOps best practices. You’ll work closely with data scientists, platform engineers, and product teams to build the infrastructure and tooling that underpin our AI capabilities, helping move models from experimentation into reliable production use. You’ll also contribute to technical standards, advocate for scalable and responsible ML development, and help shape a high-performance ML Engineering function.

ML Engineering & AI Systems:

• Own the end-to-end delivery of production machine learning and AI solutions in collaboration with data scientists and product teams
• Design and build model pipelines for training, validation, and deployment using modern tooling (e.g. MLflow, Kubernetes)
• Contribute hands-on code to model packaging, deployment, and lifecycle automation
• Build systems that monitor model performance, drift, reliability and operational health in real time
• 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

Platform & Standards:

• Help evolve our internal ML and AI platform to support experimentation, governance, and collaboration
• Define and promote best practices for ML & AI system design, includingreproducibility, testing, CI/CD, model & agent observability and evaluation
• Develop shared tools and libraries that accelerate safe, efficient, and scalable ML development

Culture & Innovation:

• Contribute to a culture of engineering excellence, collaboration, and continuous learning
• Stay up to date on emerging tools and approaches in MLOps and applied AI, helping evaluate and adopt technologies where appropriate
• Support responsible AI practices, contributing to explainability, auditability, and fairness in ML systems