Senior Machine Learning Engineer – Customer AI

Women in Data® is collaborating with Compare the Market, a purpose-driven business powered by tech and AI. They are seeking a Senior Machine Learning Engineer to own the engineering that takes ML models and AI capabilities from experimentation into reliable, scalable production use, working closely with data scientists and engineers to enhance AI systems.

Responsibilities:

• 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
• Help evolve our 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, model and agent observability and evaluation
• Develop shared tools and libraries that accelerate safe, efficient, and scalable ML development
• Work closely with data scientists to productionise experimental models and turn prototypes into robust services
• Act as a technical mentor and code reviewer for other engineers and contributors
• Provide technical leadership across ML and AI initiatives, contributing to architecture discussions and design reviews
• 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