• Function: Data & AI Solutions
• Location: Hybrid, London office

Curious about what’s next? So are we. Join Compare the Market and help to make financial decision making a breeze for millions.

At Compare the Market, we’re a purpose-driven business powered by tech and AI. We’re building high-performing, results-driven teams with the skills, mindset, and ambition to deliver outcomes at pace. Every role here plays a part in driving our mission forward, and we create an environment where you can bring your authentic self, grow a truly characterful career, and see the direct impact of your work on the lives of our customers.

We’ve carved a meerkat-shaped niche and we’re looking for ambitious, curious thinkers who thrive in a fast-moving, high-impact environment. If you love accountability, embrace challenge, and want to make a real difference, you’ll fit right in.

We’d love you to be part of our journey.

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 Senior Machine Learning Engineer, you will play a central role in making those systems production-ready. That means owning 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.

This is a hands-on role with real technical scope. You will work closely with data scientists, engineers, and product teams - contributing to architecture decisions, raising engineering standards, and building the reusable tooling that raises the pace and quality of delivery across a fast-moving and expanding set of AI capabilities and systems.

Some of the great things you'll be doing:

ML Engineering and 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 and Standards

• 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

Collaboration and Technical Leadership

• 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

Culture and 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

What we'd like to see from you:

The Essentials

• Hands-on experience with LLM-based systems: prompt engineering, RAG, tool use, or orchestration frameworks such as LangGraph or LangChain
• Familiarity with multi-step AI patterns - building systems where models plan, retrieve information and take sequences of actions
• Strong experience deploying ML models into production in cloud-native environments
• Solid software engineering skills in Python, with experience building scalable services, APIs, and production-quality code
• Experience with modern ML tooling and platforms (e.g. Databricks, MLflow, Airflow, Kubeflow, SageMaker, Vertex AI)
• Familiarity with CI/CD pipelines and infrastructure-as-code (e.g. Terraform, CloudFormation)
• Experience building robust, maintainable, and testable ML pipelines and APIs, including batch or real-time model delivery
• Strong understanding of ML lifecycle challenges - v