Azure Mlops Engineer - Inside IR35 - Onsite

Job Title: Azure MLOps Engineer

Location: Wokingham, UK
Employment Type: Contract
Duration: 6 months
Work Mode: Office-based (5 days per week)
Rate: Up to £450/day

We are looking for an experienced Azure MLOps Engineer to join a growing team responsible for building, deploying, and maintaining scalable machine learning solutions on Microsoft Azure. You will work closely with Data Scientists, Dev Ops Engineers, Architects, and Software Developers to deliver reliable, secure, and automated MLOps platforms supporting large-scale data processing and production ML workloads.

• Deploy machine learning models into Azure production environments.
• Design, implement, and maintain Azure MLOps infrastructure.
• Build and manage CI/CD pipelines for machine learning solutions using Azure Dev Ops.
• Containerize applications and ML models using Docker.
• Monitor model performance, health, and reliability in production.
• Implement logging, monitoring, and alerting solutions.
• Optimize infrastructure for scalability, performance, and cost efficiency.
• Implement automated deployment and scaling strategies.
• Manage Azure cloud resources supporting ML workloads.
• Ensure security, governance, and compliance with data protection standards.
• Manage data pipelines, storage, versioning, and lineage.
• Collaborate with cross-functional teams to support end-to-end ML life cycle management.
• Troubleshoot production issues and continuously improve platform performance.
• Maintain technical documentation and communicate effectively with both technical and non-technical stakeholders.

• 5+ years' experience in MLOps, Dev Ops, or a related field.
• Strong experience with Azure Machine Learning.
• Hands-on experience with Azure Dev Ops CI/CD pipelines.
• Strong Python programming skills.
• Experience with Docker and containerized deployments.
• Good understanding of the machine learning life cycle and production deployment.
• Experience with Azure SQL Database, Azure Storage (Blob Storage), and SQL/No
  
  SQL databases.
• Experience with machine learning frameworks such as Tensor Flow, PyTorch, or Scikit-learn.
• Experience monitoring and supporting production ML models.
• Knowledge of infrastructure automation and scalable cloud architectures.
• Experience supporting Real Time inference using Azure Machine Learning.

• Azure Data Scientist Associate certification.
• Experience with data engineering tools and practices.
• Familiarity with GRIB, NetCDF, Parquet, and JSON data formats.
• Experience working with enterprise MLOps frameworks.