Machine Learning Engineer

1 month ago


Westminster Abbey, United Kingdom Fynity Full time
About the Opportunity

We are seeking a skilled Machine Learning Engineer to join our dynamic and rapidly growing specialist insurance business. This is a pivotal role where you will contribute significantly to the development and deployment of data science applications.

Key Responsibilities
  • Develop and deploy machine learning models into production environments.
  • Maintain and enhance existing applications and data pipelines.
  • Collaborate closely with the Data Science team to deliver high-value solutions.
  • Ensure high-quality, robust, and tested code that serves model predictions effectively.
  • Focus on microservices architecture to achieve high volume and low latency performance.
  • Implement and manage Continuous Integration and Continuous Deployment (CI/CD) processes.
  • Support the Data Science team by taking on technical tasks as required.
Who You Are

The ideal candidate will have:

  • 3+ years of experience in ML, Software Engineering, or MLOps.
  • Demonstrable experience in microservices architecture, particularly in high volume, low latency environments.
  • Strong Python programming skills and experience with Python web frameworks.
  • Proven experience in testing and CI/CD practices.
  • Experience with Kubernetes (K8) for container orchestration.
  • Familiarity with Docker, with the ability to pick it up quickly.
  • A solid foundation in software engineering, with a strong coding background.
  • A strong focus on reliability and testing, committed to delivering high-quality solutions.
Why Consider This Opportunity
  • Competitive salary range of £65,000 to £80,000, depending on experience.
  • Be part of a rapidly expanding team with significant opportunities for growth and innovation.
  • Work in a flexible, hybrid environment that values a proactive attitude.
  • Contribute to a leading business reshaping the insurance landscape.
  • Become part of a collaborative team culture that values diverse perspectives and recognises individual contributions.


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