Machine Learning Engineer
Role Overview
Turn machine learning models into production-grade solutions for business and customer use.
Work across software engineering, MLOps, and data science to build frameworks, pipelines, and services for deployment, monitoring, and scale.
Help transform experimental code into robust, maintainable, secure production solutions.
Key Skills for This Role
Full Job Posting
About the role
Turn machine learning models into production-grade solutions for business and customer use.
Work across software engineering, MLOps, and data science to build frameworks, pipelines, and services for deployment, monitoring, and scale.
Help transform experimental code into robust, maintainable, secure production solutions.
Key responsibilities
- Partner with data scientists to move models from experimentation into scalable production applications and services.
- Design and maintain MLOps frameworks, deployment pipelines, and automated model lifecycle processes.
- Develop CI/CD pipelines, version control frameworks, and automated testing for ML solutions.
- Build APIs, services, and integrations for business systems and customer-facing applications.
- Implement model monitoring, performance tracking, drift detection, and automated retraining.
- Use cloud-native technologies including Azure Databricks, MLflow, and related platforms.
- Collaborate with data engineers, architects, and developers on secure, scalable ML solutions.
About you
- Proven experience deploying and supporting machine learning solutions in production.
- Strong version control knowledge, including Git, branching strategies, and release management.
- Strong Python engineering skills, including testing, code quality, and maintainability.
- Experience with CI/CD, MLOps frameworks, and automated ML deployment.
- Hands-on experience with Azure Databricks or a similar platform such as MLflow, KubeFlow, or SageMaker.
- Experience building APIs, microservices, and cloud-based integrations for machine learning.
- Strong collaboration and technical communication skills across multidisciplinary teams.
Rewards and benefits
- Competitive salary, annual discretionary bonus, pension, life assurance, 25 days of annual leave plus bank holidays, and enhanced family leave.
- Personal and professional development support.
- Hybrid working, with a blend of Support Centre and home working.
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