Machine Learning Engineer

Compare the Market; Cambridgeshire and Peterborough; On-site; GBP 55,000 - 75,000; Full time

Job summary

A leading financial services company in the UK is seeking a Machine Learning Engineer to develop and maintain machine learning pipelines and collaborate with data scientists. This role offers hands-on experience in a fast-paced environment, focusing on deploying models, building monitoring systems, and supporting an evolving ML platform. Ideal candidates will possess strong Python skills and a background in software engineering or a quantitative field. The position also emphasizes collaboration and mentorship within the team.

Qualifications

• Practical experience deploying ML models into production environments.
• Strong Python development skills and understanding of ML model structures.
• Familiarity with CI/CD concepts and basic infrastructure automation.

Responsibilities

• Develop and maintain machine learning pipelines for training, validation, and deployment.
• Collaborate with data scientists to productionise models.
• Build monitoring and alerting for model health and performance.

Skills

ML model deployment

Python development

Collaboration skills

Growth mindset

Education

Software engineering, computer science, or quantitative field

Tools

MLflow

Airflow

SageMaker

Vertex AI

Job description

Why this role matters At Compare the Market, we're applying AI to real-world problems that help millions of people make smarter financial decisions. As a Machine Learning Engineer, you'll work at the heart of this transformation-building the infrastructure and tooling that enables our data scientists to move from prototype to production quickly, safely, and at scale. You'll be part of a growing ML Engineering team, contributing to a modern MLOps platform and delivering robust ML services in collaboration with product, engineering, and data science colleagues. This is a hands-on role that's ideal for someone who wants to grow in a high-impact environment with strong mentorship and real ownership. What you’ll be doing ML Engineering & Deployment

Responsibilities

• Develop and maintain machine learning pipelines for training, validation, and deployment
• Collaborate with data scientists to productionise models and turn prototypes into performant, reliable services
• Contribute to deployment tooling and automation for both batch and real-time ML use cases
• Build monitoring and alerting for model health, performance, and data drift

Platform & Standards

• Support the evolution of our internal ML platform and development workflows
• Apply best practices in testing, CI/CD, version control, and infrastructure-as-code
• Contribute to team libraries, reusable components, and shared deployment patterns

Collaboration & Growth

• Work in cross-functional teams alongside product managers, engineers, and analysts
• Participate in design sessions, peer reviews, and sprint planning
• Learn from and be mentored by experienced ML Engineers and technical leaders

Qualifications

Must Have

• Practical experience deploying ML models into production environments
• Strong Python development skills and understanding of ML model structures
• Familiarity with tools such as MLflow, Airflow, SageMaker, or Vertex AI
• Understanding of CI/CD concepts and basic infrastructure automation
• Ability to write well-tested, maintainable, and modular code
• Strong collaboration skills and a growth mindset
• A background in software engineering, computer science, or a quantitative field-or equivalent hands‑on experience in ML delivery

Nice to Have

• Experience working in regulated sectors such as insurance, banking, or financial services
• Exposure to Databricks, container orchestration (e.g. Kubernetes), or workflow engines (e.g. Argo, Airflow)
• Familiarity with real-time model deployment, streaming data, or event‑driven systems (e.g. Kafka, Flink)
• Interest in MLOps, model governance, and responsible AI practices
• Understanding of basic model evaluation, drift detection, and monitoring techniques