Azure Devops Engineer, terraform, MLops
Posted Today by GCS
Negotiable
Undetermined
Hybrid
London, UK
Summary: The Azure Platform Engineer role focuses on leveraging data engineering and MLOps expertise to build and optimize Azure-based infrastructure for large-scale data and machine learning workloads. The position is ideal for engineers who excel in automation, scalability, and cloud-native design, working within a dynamic data platform team. The role requires collaboration with various teams to implement robust automated solutions. This position is hybrid, allowing for both on-site and remote work within the UK.
Key Responsibilities:
- Design, build, and maintain Azure data and ML platform infrastructure
- Develop and manage CI/CD pipelines using Azure DevOps and Terraform
- Automate provisioning and configuration of Azure resources (Data Factory, Synapse, Databricks, Key Vault, etc.)
- Implement MLOps best practices for model training, deployment, and monitoring
- Collaborate with Data Scientists, ML Engineers, and Platform teams to deliver robust, automated solutions
Key Skills:
- Strong experience across Azure Data Services (ADF, Synapse, Databricks, Azure ML)
- Deep understanding of cloud networking, security, and governance
- Terraform - advanced use of modules, workspaces, and state management
- Automating resource deployment and scaling in enterprise Azure environments
- Azure ML, MLflow, or Databricks MLOps frameworks
- Building automated pipelines for model training and deployment
- Integration of CI/CD with data and ML workloads
Salary (Rate): undetermined
City: London
Country: UK
Working Arrangements: hybrid
IR35 Status: undetermined
Seniority Level: undetermined
Industry: IT
Azure Platform Engineer - Terraform/MLOps - Hybrid
Location: Hybrid (London/Remote - UK Based)
Start Date: ASAP
Duration: 6 months initial
Overview:
We are looking for a talented Azure Platform Engineer with solid data engineering and MLOps experience to join a forward-thinking data platform team. You will play a key role in building and optimising Azure-based infrastructure to support large-scale data and machine learning workloads.
This role sits at the intersection of DataOps, DevOps, and MLOps - ideal for engineers who thrive on automation, scalability, and cloud-native design.
Key Responsibilities:
- Design, build, and maintain Azure data and ML platform infrastructure
- Develop and manage CI/CD pipelines using Azure DevOps and Terraform
- Automate provisioning and configuration of Azure resources (Data Factory, Synapse, Databricks, Key Vault, etc.)
- Implement MLOps best practices for model training, deployment, and monitoring
- Collaborate with Data Scientists, ML Engineers, and Platform teams to deliver robust, automated solutions
Essential Skills & Experience:
Azure Expertise:
- Strong experience across Azure Data Services (ADF, Synapse, Databricks, Azure ML)
- Deep understanding of cloud networking, security, and governance
Infrastructure as Code (IaC):
- Terraform - advanced use of modules, workspaces, and state management
- Automating resource deployment and scaling in enterprise Azure environments
MLOps/DataOps:
- Azure ML, MLflow, or Databricks MLOps frameworks
- Building automated pipelines for model training and deployment
- Integration of CI/CD with data and ML workloads
Please send across an updated CV if this is of interest
GCS is acting as an Employment Business in relation to this vacancy.
