Azure AI DevOps Engineer - GBP 50000

Role: Azure AI DevOps Engineer
Salary: £50,000
Location: London or remote
Benefits: Healthcare and great holidays

The Role

As a Machine Learning Operations Engineer, you will be instrumental in designing, deploying, and maintaining machine learning systems that drive data-driven decision-making in the pensions and financial services sector. Using Azure's cloud ecosystem, you will build scalable ML workflows that support actuarial modelling, risk prediction, and client insight generation.

Key Responsibilities

1. Azure-Based Machine Learning Development
* Collaborate with domain experts and analysts to design and implement machine learning models that predict key outcomes such as life expectancy, investment return scenarios, and financial risks.
* Deploy and manage ML solutions using Azure Machine Learning (Azure ML), covering training, testing, and deployment to production.
* Select appropriate ML techniques (e.g., classification, regression, ensemble methods, survival models) to improve predictions and automate core processes.

2. MLOps & Model Lifecycle Management
* Own the end-to-end ML lifecycle using Azure ML pipelines, Data Factory (ADF), and ML tracking tools.
* Implement CI/CD pipelines with Azure DevOps, ensuring robust version control, testing, and seamless deployment.
* Monitor production ML systems for performance, data drift, and anomalies using Azure Monitor or other observability tools.
* Schedule and automate model retraining pipelines to maintain performance over time.

3. Data Engineering & Preprocessing
* Develop and maintain scalable ETL/ELT data pipelines using Azure Data Factory, Data Lake, and SQL.
* Prepare and preprocess large datasets to ensure data quality and readiness for ML modelling and analytics workflows.

4. Software Engineering & Collaboration
* Write clean, maintainable, and production-ready code in Python using industry-standard libraries and tools.
* Work collaboratively with data scientists, actuarial teams, and software developers to integrate ML outputs into end-user solutions.
* Contribute to shared tools, templates, and frameworks to support faster development cycles and collaboration.

5. Communication, Training & Innovation
* Clearly communicate ML model behaviour, performance, and limitations to both technical and non-technical stakeholders.
* Support and upskill colleagues in the use of Azure ML tools and MLOps practices.
* Explore and propose innovations based on emerging trends in Azure AI/ML and financial analytics.

Essential Criteria

* Demonstrated experience deploying and managing production machine learning models using Azure Machine Learning.
* Strong grasp of MLOps principles, including CI/CD, model monitoring, and retraining pipelines.
* Experience with Python, SQL, and Azure data services such as Data Factory, Blob Storage, or Data Lake.
* Proficiency with DevOps tools (e.g., Git, Azure DevOps) and version control workflows.
* Familiarity with reporting and visualisation tools (e.g., Power BI).
* Strong communication skills, with the ability to translate technical detail into business-relevant insights.
* Experience working in regulated environments such as pensions, insurance, or financial services is highly advantageous.

Desirable

* Exposure to actuarial modelling or financial risk analysis.
* Familiarity with Azure Databricks, Synapse, or distributed computing tools.
* Experience applying model explainability and responsible AI techniques.

Rewards & Benefits
This role offers a comprehensive and flexible benefits package designed to support wellbeing and personal development, including:

* Competitive salary
* Annual discretionary bonus
* 25 days holiday, with option to buy/sell
* Flexible bank holidays
* Matched pension contributions
* Healthcare cash plan and digital GP access
* Life assurance (4x salary)
* Employee Assistance Programme (EAP)
* High street discounts and retail offers
* Paid volunteering day
* Flexible benefits to support lifestyle, health, and protection needs
* Staff referral scheme