Machine Learning Ops Engineer

About the Company A leading UK consulting and administration business specialising in pensions and insurance services. The organisation combines deep industry expertise with advanced technology and analytics to support large-scale pension schemes and their sponsoring employers. It provides administration for over one million members and delivers advisory services across schemes of all sizes, including many with assets exceeding £1bn. It also supports insurance clients in the life and bulk annuities sector. Package Details Remote (UK) | £45,000–£60,000 + 6% bonus Main Duties and Responsibilities

Model Development (Azure Machine Learning Studio focus) Work collaboratively with actuarial and analytics teams to design, build, and deploy machine learning and statistical models using Azure Machine Learning Studio (AML Studio) in production environments. Apply appropriate ML techniques to improve predictions such as longevity, default risk, and investment outcomes.

Machine Learning Operations (MLOps in Azure) Manage the full ML lifecycle using Azure ML Studio, including deployment, monitoring, retraining pipelines, and version control. Implement robust MLOps practices such as model drift detection, data quality monitoring, and automated retraining workflows.

Data Engineering and Preprocessing Develop and maintain scalable data pipelines using Python, SQL, and Azure Data Factory (ADF). Ensure data is clean, reliable, and structured for use in Azure ML Studio workflows.

Software Development Produce clean, efficient, and production-grade Python code. Apply CI/CD practices and DevOps/MLOps principles integrated with Azure Machine Learning Studio environments.

Cross-functional Collaboration Work closely with actuarial analysts and modelling teams to translate outputs from Azure ML Studio into actionable insights and business recommendations.

Innovation and Continuous Improvement Stay up to date with developments in Azure Machine Learning Studio, MLOps, and data science technologies, identifying opportunities to improve models, automation, and delivery efficiency.

Training and Knowledge Sharing Support and train team members on machine learning approaches and Azure ML Studio workflows, including deployment and monitoring practices.

Stakeholder Communication Clearly explain machine learning concepts and Azure ML Studio-based solutions to both technical and non-technical stakeholders.

Job RequirementsEssential

Strong hands-on experience with Azure Machine Learning Studio (AML Studio), including end-to-end model development, deployment, monitoring, and lifecycle management in production environments

Experience building and optimising ML models using Azure ML workflows

Strong Python and SQL skills for data manipulation, modelling, and automation

Experience with Azure Data Factory (ADF) for data pipeline development

Strong understanding of CI/CD and MLOps practices, ideally within Azure environments

Experience with data visualisation tools such as Power BI

Strong communication skills with ability to explain technical concepts clearly to non-technical audiences

Desirable

Experience in pensions, insurance, or regulated financial services

Experience working in multidisciplinary analytics or actuarial teams

Broader exposure to Azure ecosystem tools (e.g., Azure DevOps, Databricks)

Key Requirement The most critical requirement for this role is hands-on, production-level experience with Azure Machine Learning Studio (AML Studio), including building and deploying ML models end-to-end, managing model lifecycle in production, implementing MLOps workflows (monitoring, drift detection, retraining), and integrating Azure ML Studio with data pipelines and CI/CD processes.

Due to the volume of applications received for positions, it will not be possible to respond to all applications and only applicants who are considered suitable for interview will be contacted.

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