Senior Data Engineer (Machine Learning (ML)

Summary

Senior Data Engineer (ML) builds and scales production-grade AI systems from prototypes, leading MLOps, CI/CD, and model monitoring alongside data scientists in a government team.

Can you work with technical and non-technical stakeholders to deliver data-driven outcomes?

Do you enjoy solving complex data engineering and machine learning challenges?

Have you built and optimised data pipelines that support analytical and AI use cases?

If so, we’d love to hear from you!

A Senior Data Engineer (ML) works within a rapid prototyping team alongside data scientists to develop, test, and scale data science solutions. They are responsible for ensuring that prototypes are production-ready, scalable, and maintainable. The role bridges data science and data engineering, enabling the team to deliver high-impact, operational AI solutions. Joining our department comes with many benefits, including:

Employer pension contribution of 28.97% of your salary. Read more about Civil Service Pensions here
25 days annual leave, increasing by 1 day each year of service (up to a maximum of 30 days annual leave), plus 8 bank holidays and a privilege day for the King’s birthday
Flexible working options where we encourage a great work-life balance.

Read more in the Benefits section below!

Find out more about what it's like working at: MCA Homepage | Civil Service Careers

As our Senior Data Engineer (ML), you’ll transform data science prototypes into secure, scalable, production-ready solutions. Working alongside data scientists, you’ll lead MLOps, CI/CD and model development and drift practices, optimise machine learning pipelines, champion responsible AI, and establish engineering standards that will support the Agency’s growing data and AI capability

Your responsibilities will include, but aren’t limited to:

Work within the data science team to deliver rapid prototypes for new solutions, developing new solutions using cutting-edge techniques.
Collaborate with and translate data science prototypes into scalable, production-grade machine learning systems using robust engineering practices and automation pipelines.
Design and implement scalable machine learning pipelines using modern MLOps tools and frameworks. Utilising Prototype-to-Production engineering.
Apply internal data engineering best practices to ensure data quality, lineage, and reproducibility.
Maintain continuous integration and deployment workflows for machine learning models, including automated testing, validation, and monitoring. Use tools such as MLflow, Databricks, and infrastructure-as-code to ensure reproducibility and reliability.
Monitor deployed models for performance degradation, drift, and fairness. Implement retraining strategies and version control to maintain model integrity over time.

For further information on the role, please read the role profile.

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