Machine Learning Engineer

Summary: We are seeking a Machine Learning Engineer with extensive experience in large-scale, cloud-based projects, particularly in the financial sector. The candidate will be responsible for operationalising ML models using Databricks MLFlow and implementing MLOps practices to ensure model scalability and maintainability. Expertise in model auditing and reproducibility is essential for this role. The position requires collaboration with Data Scientists and the development of robust ML pipelines.

Key Responsibilities:

• Collaborate with Data Scientists to operationalise ML models with full auditing and reproducibility.
• Develop and manage scalable ML pipelines in Databricks using best practices.
• Ensure modular model design with proper version control, parameterisation, and input tracking.
• Implement model logging, monitoring, and alerting mechanisms for robust error handling.
• Automate model selection processes (champion/challenger approach) using experiment tracking.
• Schedule and orchestrate model runs based on dependencies and business rules.
• Build and maintain reusable ML frameworks (Python, R, MATLAB templates).
• Detect and manage data drift, concept drift, and model performance degradation.
• Implement CI/CD pipelines for ML workflows and automate deployments.
• Create and maintain comprehensive documentation.

Key Skills:

• Bachelor's degree in Computer Science, Data Science, Engineering, or a related field.
• 5+ years of experience in Machine Learning Engineering, with large-scale business applications.
• Expertise in Databricks MLFlow and MLOps best practices.
• Strong Python skills; familiarity with R and MATLAB is a plus.
• Hands-on experience with time series data analysis and business process orchestration.
• Knowledge of data governance, version control, and model auditability.
• Experience working in the financial/banking sector; monetary policy knowledge is a plus.
• Familiarity with CI/CD, Agile delivery, and cloud environments (preferably Azure).
• Strong communication, problem-solving, and documentation skills.

Salary (Rate): undetermined

City: London

Country: UK

Working Arrangements: hybrid

IR35 Status: undetermined

Seniority Level: undetermined

Industry: IT

Detailed Description From Employer:

Job Summary:

We are looking for a skilled Machine Learning Engineer with proven experience in large-scale, cloud-based programmes, especially within the financial sector. The ideal candidate will be an expert in operationalising models using Databricks MLFlow, implementing end-to-end MLOps practices, and ensuring models are auditable, scalable, and maintainable.

Key Responsibilities:

• Collaborate with Data Scientists to operationalise ML models with full auditing and reproducibility.
• Develop and manage scalable ML pipelines in Databricks using best practices.
• Ensure modular model design with proper version control, parameterisation, and input tracking.
• Implement model logging, monitoring, and alerting mechanisms for robust error handling.
• Automate model selection processes (champion/challenger approach) using experiment tracking.
• Schedule and orchestrate model runs based on dependencies and business rules.
• Build and maintain reusable ML frameworks (Python, R, MATLAB templates).
• Detect and manage data drift, concept drift, and model performance degradation.
• Implement CI/CD pipelines for ML workflows and automate deployments.
• Create and maintain comprehensive documentation.

Required Skills & Experience:

• Bachelor's degree in Computer Science, Data Science, Engineering, or a related field.
• 5+ years of experience in Machine Learning Engineering, with large-scale business applications.
• Expertise in Databricks MLFlow and MLOps best practices.
• Strong Python skills; familiarity with R and MATLAB is a plus.
• Hands-on experience with time series data analysis and business process orchestration.
• Knowledge of data governance, version control, and model auditability.
• Experience working in the financial/banking sector; monetary policy knowledge is a plus.
• Familiarity with CI/CD, Agile delivery, and cloud environments (preferably Azure).
• Strong communication, problem-solving, and documentation skills.