Lead Machine Learning Engineer - Model Deployment & MLOps - New Team Build

GL Global; United Kingdom; On-site; GBP 90,000 - 130,000; Full time

Job summary

GL Global Limited is seeking a Lead Machine Learning Engineer to own the deployment of ML solutions into production and scale them across platforms.

You will bridge data science and engineering, building robust ML pipelines, managing model lifecycles and ensuring reliable deployment and monitoring of models in production environments.

Qualifications

• Strong commercial experience in Machine Learning Engineering or MLOps.
• Advanced Python and SQL development skills.
• Proven experience deploying and operationalising machine learning models.
• Hands-on experience with Databricks, including notebooks, workflows and Delta Lake.
• Experience with MLflow, model registries and ML lifecycle management.
• Understanding of CI/CD, Git and Azure DevOps.
• Experience building scalable, reliable and maintainable machine learning solutions.

Responsibilities

• Leading the deployment and operationalisation of machine learning models into production environments.
• Building and maintaining scalable ML pipelines using Python, SQL and Databricks.
• Implementing MLOps practices covering model lifecycle management, versioning, deployment and monitoring.
• Working with MLflow, model registries and production scoring frameworks.
• Developing and optimising Databricks workflows, notebooks and Delta Lake solutions.
• Establishing repeatable processes for model deployment, testing, monitoring and continuous improvement.
• Collaborating with Data Scientists, Data Engineers and wider technology teams to deliver production-grade ML solutions.

Skills

Machine Learning Engineering

MLOps

Python

SQL

CI/CD

Git

Azure DevOps

Model deployment

Production ML

Tools

Databricks

Delta Lake

MLflow

Job description

We're looking for a talented Lead Machine Learning Engineer to join a major international organisation investing heavily in its data science, machine learning and AI capabilities.

This is an exciting opportunity to take ownership of the engineering and deployment of machine learning solutions, moving models beyond experimentation and into scalable, production-ready environments.

You'll play a key role in bridging the gap between data science and engineering, developing robust ML pipelines, managing model lifecycles and ensuring machine learning solutions can be reliably deployed, monitored and scaled.

What you'll be working on:

• Leading the deployment and operationalisation of machine learning models into production environments.
• Building and maintaining scalable ML pipelines using Python, SQL and Databricks.
• Implementing MLOps practices covering model lifecycle management, versioning, deployment and monitoring.
• Working with MLflow, model registries and production scoring frameworks.
• Developing and optimising Databricks workflows, notebooks and Delta Lake solutions.
• Establishing repeatable processes for model deployment, testing, monitoring and continuous improvement.
• Collaborating with Data Scientists, Data Engineers and wider technology teams to deliver production-grade ML solutions.

Experience:

• Strong commercial experience in Machine Learning Engineering or MLOps.
• Advanced Python and SQL development skills.
• Proven experience deploying and operationalising machine learning models.
• Hands-on experience with Databricks, including notebooks, workflows and Delta Lake.
• Experience with MLflow, model registries and ML lifecycle management.
• Understanding of CI/CD, Git and Azure DevOps.
• Experience building scalable, reliable and maintainable machine learning solutions.

Additional experience that would be beneficial:

• Databricks Asset Bundles and Databricks Apps.
• Model monitoring, performance tracking and automated retraining.

If you're a Machine Learning Engineer or MLOps specialist who enjoys taking models from development through to production, building robust ML infrastructure and delivering solutions at scale, we'd love to hear from you.