Azure MLOps Engineer

Key Responsibilities

• Deploy machine learning models into production environments in collaboration with Data Scientists and Forecasters.
• Implement and maintain scalable, secure, and highly available Azure MLOps infrastructure.
• Follow established deployment strategies to ensure safe and controlled production releases.
• Design and manage Azure cloud resources required for model hosting and inference.
• Utilise Docker and containerisation technologies to package models and dependencies.
• Establish monitoring, logging, and alerting solutions to track model health, performance, and reliability.
• Continuously monitor, maintain, and optimise production ML models.
• Improve scalability and cost efficiency across cloud infrastructure and ML workloads.
• Implement auto-scaling capabilities and parallel processing mechanisms to support fluctuating demand.
• Ensure security best practices and compliance with data governance and regulatory requirements.
• Manage data pipelines and storage solutions supporting model training and inference workloads.
• Implement data versioning and lineage tracking to ensure data integrity and traceability.
• Work collaboratively with engineering, DevOps, and business stakeholders to deliver robust ML solutions.
• Identify system bottlenecks and drive continual performance improvements.
• Produce and maintain clear technical documentation covering deployments, configurations, and architecture.

Essential Experience

• 5+ years' experience in MLOps, DevOps, Machine Learning Engineering, or a related field.
• Strong understanding of machine learning concepts and model lifecycle management.
• Demonstrable experience building and automating cloud-based ML platforms.
• Deep understanding of software engineering principles and ML model deployment.
• Extensive experience with Azure Machine Learning and Azure cloud services.
• Strong Python development skills.
• Experience working with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Hands-on experience creating CI/CD pipelines and release processes using Azure DevOps.
• Experience supporting real-time inference and production ML environments.
• Strong knowledge of monitoring and observability practices for ML workloads.
• Experience with SQL and NoSQL databases.
• Strong knowledge of Azure SQL Database and Azure Storage Accounts, including Blob Storage.

Desirable Skills

• Experience with MLOps frameworks and tooling.
• Familiarity with data engineering concepts and modern data platforms.
• Knowledge of tools, methodologies, and frameworks used by Data Scientists.
• Experience working with data formats including Parquet, JSON, GRIB, and NetCDF.
• Azure Data Scientist Associate certification.
• Experience optimising large-scale ML workloads in Azure environments.