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Machine Learning Engineer
- Aggregated by CV-Library
- Wokingham, Berkshire
- Contract
- Full Time
- £167,900 - £167,900 Per Day
Job Description
Location: Wokingham - Office based (hybrid working with 3+ days per week onsite may be considered)
Start Day: ASAP
Contract Rate: £460 per day inside IR35
Duration: 6 months initially
Role Overview
Our client is seeking an experienced Azure MLOps Engineer to support the deployment, automation, and management of machine learning solutions within Azure. Working closely with architects, data scientists, forecasting teams, developers, and DevOps engineers, you will help deliver scalable, secure, and reliable MLOps platforms supporting large-scale data processing and real-time inference workloads.
Key Responsibilities
* Deploy and manage ML models in production using Azure Machine Learning.
* Design and maintain Azure-based MLOps infrastructure.
* Build and support Azure DevOps CI/CD pipelines for ML artefacts.
* Implement monitoring, logging, security, and governance controls.
* Manage data pipelines, storage solutions, data versioning, and lineage tracking.
* Support real-time inference and scalable ML workloads, including auto-scaling.
* Collaborate with technical and business stakeholders to optimise model performance and platform reliability.
* Produce and maintain technical documentation.
Skills & Experience
* 5+ years' experience in MLOps, DevOps, or related engineering roles.
* Strong knowledge of the ML lifecycle and production ML operations.
* Hands-on experience with Azure Machine Learning and MLOps frameworks.
* Experience with Azure DevOps, CI/CD pipelines, and automation.
* Strong Python skills and experience with TensorFlow, PyTorch, or Scikit-learn.
* Experience with Docker, Azure SQL Database, Storage Accounts, Blob Storage, and SQL/NoSQL technologies.
* Experience monitoring and supporting production ML environments.
* Knowledge of data engineering practices and tools.
* Familiarity with GRIB, NetCDF, Parquet, and JSON is advantageous.
* Azure Data Scientist Associate certification is desirable.
If you have the relevant skills and experience, please do apply promptly to be considered
Start Day: ASAP
Contract Rate: £460 per day inside IR35
Duration: 6 months initially
Role Overview
Our client is seeking an experienced Azure MLOps Engineer to support the deployment, automation, and management of machine learning solutions within Azure. Working closely with architects, data scientists, forecasting teams, developers, and DevOps engineers, you will help deliver scalable, secure, and reliable MLOps platforms supporting large-scale data processing and real-time inference workloads.
Key Responsibilities
* Deploy and manage ML models in production using Azure Machine Learning.
* Design and maintain Azure-based MLOps infrastructure.
* Build and support Azure DevOps CI/CD pipelines for ML artefacts.
* Implement monitoring, logging, security, and governance controls.
* Manage data pipelines, storage solutions, data versioning, and lineage tracking.
* Support real-time inference and scalable ML workloads, including auto-scaling.
* Collaborate with technical and business stakeholders to optimise model performance and platform reliability.
* Produce and maintain technical documentation.
Skills & Experience
* 5+ years' experience in MLOps, DevOps, or related engineering roles.
* Strong knowledge of the ML lifecycle and production ML operations.
* Hands-on experience with Azure Machine Learning and MLOps frameworks.
* Experience with Azure DevOps, CI/CD pipelines, and automation.
* Strong Python skills and experience with TensorFlow, PyTorch, or Scikit-learn.
* Experience with Docker, Azure SQL Database, Storage Accounts, Blob Storage, and SQL/NoSQL technologies.
* Experience monitoring and supporting production ML environments.
* Knowledge of data engineering practices and tools.
* Familiarity with GRIB, NetCDF, Parquet, and JSON is advantageous.
* Azure Data Scientist Associate certification is desirable.
If you have the relevant skills and experience, please do apply promptly to be considered