Machine Learning Engineer
On-siteYork, England, United Kingdom
Job Summary
Develop and maintain infrastructure for deploying machine learning models in real-time and batch environments, building Python APIs to serve these services. Design and implement CI/CD pipelines for automated model deployment while writing unit tests to ensure code quality and maintainability. Collaborate with data scientists and platform engineers to integrate ML solutions into user-facing applications and oversee the automation of the data science lifecycle from dataset build to production monitoring. Manage cloud-based ML services on Azure and GCP, ensuring reliability, performance, and adherence to infrastructure best practices.
Required Qualifications
- Bachelor's/Master's degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Physics, Engineering) or equivalent
- 3-5 years as an ML engineer
- Good understanding of core data science principles and understanding of challenges of migrating research code into production code
- Hands on experience in machine learning engineering, including deploying, monitoring, and maintaining ML models in production environments (Neural networks, Random forests etc.)
- Solid experience as a Python developer, ideally in a machine learning engineering context (Flask/FastAPI, OOP, unit testing)
- Strong understanding of software engineering best practice
- Experience with TDD
- Experience with infrastructure as code tools like Terraform.or similar
- Hands on experience with cloud platforms (GCP, AWS, or Azure)
- Familiarity with containerization using Docker and orchestration of deployments
- Experience with CI/CD tools and Git-based development workflows
- Understanding of API operations monitoring and logging
- Strong problem-solving skills and ability to work independently on technical tasks
- Familiarity with Agile methodologies and experience working in Agile teams
Desired Qualifications
- Experience in financial services or insurance is an advantage but not required
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