MLOps Engineer – Azure
MLOps Engineer – Azure | FinTech
• Location: Remote / Hybrid (Preferred)
• Contract Type: 6-Month Contract (with potential for extension)
• Start Date: ASAP
About Us:
We are a leading FinTech company transforming digital finance through cutting-edge technology and innovative solutions. As we scale our data and machine learning (ML) capabilities, we're seeking an experienced MLOps Engineer to join our dynamic team. In this role, you will bridge the gap between machine learning models and production-ready systems, leveraging Azure to deploy, monitor, and automate our ML pipelines.
Key Responsibilities:
• Design, implement, and manage end-to-end MLOps pipelines for machine learning model deployment, monitoring, and automation using Azure Machine Learning (AML).
• Leverage Azure DevOps for creating CI/CD pipelines for model training, validation, deployment, and version control.
• Develop robust data pipelines using Azure Data Factory and Azure Databricks for ML workflows.
• Deploy machine learning models to Azure Kubernetes Service (AKS), Azure Container Instances (ACI), or Azure Functions.
• Implement monitoring and logging solutions for ML models using Azure Monitor, Log Analytics, and Application Insights.
• Ensure models are compliant with FinTech industry standards, addressing performance, security (Security API, Secret Management), and auditability.
• Work closely with data scientists and software engineers to integrate and optimise models into production systems.
• Provide training and documentation on maintaining the MLOps pipelines and monitoring systems.
Key Deliverables:
• End-to-End MLOps Pipeline: Fully automated pipeline that incorporates model development, versioning, deployment, and monitoring using Azure Machine Learning, Azure DevOps, and Terraform.
• Model Deployment Framework: Deployment of machine learning models to production environments on Azure Kubernetes Service (AKS), ACI, or Azure Functions, ensuring scalability and high availability.
• Automated Data Pipelines: Integration of data pipelines with Azure Data Factory and Azure Synapse for robust ML data management and preprocessing.
• Real-Time Monitoring: Implementation of real-time monitoring and alerts for model performance and drift using Azure Monitor, Application Insights, and Log Analytics.
• Model Versioning and Governance: Setup of model version control, automated workflows for model retraining, and performance tracking through AML and Azure DevOps.
• Documentation & Knowledge Sharing: Creation of comprehensive documentation for MLOps pipelines and deployment processes to ensure seamless handover and transparency.
Required Skills & Experience:
• 3+ years of hands-on experience in MLOps, DevOps, or Cloud Engineering roles, with a strong emphasis on Azure environments.
• Expertise in Azure Machine Learning (AML), including model management, AutoML, model deployment, and experiment tracking.
• Experience in building and maintaining CI/CD pipelines using Azure DevOps and version control systems like Git.
• Strong knowledge of Azure Kubernetes Service (AKS), Azure Container Instances (ACI), and Azure Functions for containerised model deployment.
• Proven experience with Azure Data Factory, Azure Databricks, and Azure Synapse Analytics for managing and orchestrating data pipelines.
• Familiarity with Terraform for infrastructure as code (IaC) and automated provisioning of Azure resources.
• Proficient in Python, with experience in scikit-learn, TensorFlow, PyTorch, or XGBoost for machine learning tasks.
• Solid understanding of model monitoring and logging using Azure Monitor, Application Insights, and Log Analytics.
• Knowledge of best practices for model governance, versioning, and deployment in a regulated FinTech environment.
Nice to Have:
• Azure certifications (e.g., Azure AI Engineer, Azure Solutions Architect).
• Experience with MLflow, Kubeflow, or other model management frameworks.
• Familiarity with containerisation and orchestration tools like Docker, Kubernetes, and Helm.
• Experience in data security and compliance standards relevant to the FinTech industry, including PCI DSS or SOC 2 compliance.
MLOps Engineer – Azure | FinTech
• Location: Remote / Hybrid (Preferred)
• Contract Type: 6-Month Contract (with potential for extension)
• Start Date: ASAP
About Us:
We are a leading FinTech company transforming digital finance through cutting-edge technology and innovative solutions. As we scale our data and machine learning (ML) capabilities, we're seeking an experienced MLOps Engineer to join our dynamic team. In this role, you will bridge the gap between machine learning models and production-ready systems, leveraging Azure to deploy, monitor, and automate our ML pipelines.
Key Responsibilities:
• Design, implement, and manage end-to-end MLOps pipelines for machine learning model deployment, monitoring, and automation using Azure Machine Learning (AML).
• Leverage Azure DevOps for creating CI/CD pipelines for model training, validation, deployment, and version control.
• Develop robust data pipelines using Azure Data Factory and Azure Databricks for ML workflows.
• Deploy machine learning models to Azure Kubernetes Service (AKS), Azure Container Instances (ACI), or Azure Functions.
• Implement monitoring and logging solutions for ML models using Azure Monitor, Log Analytics, and Application Insights.
• Ensure models are compliant with FinTech industry standards, addressing performance, security (Security API, Secret Management), and auditability.
• Work closely with data scientists and software engineers to integrate and optimise models into production systems.
• Provide training and documentation on maintaining the MLOps pipelines and monitoring systems.
Key Deliverables:
• End-to-End MLOps Pipeline: Fully automated pipeline that incorporates model development, versioning, deployment, and monitoring using Azure Machine Learning, Azure DevOps, and Terraform.
• Model Deployment Framework: Deployment of machine learning models to production environments on Azure Kubernetes Service (AKS), ACI, or Azure Functions, ensuring scalability and high availability.
• Automated Data Pipelines: Integration of data pipelines with Azure Data Factory and Azure Synapse for robust ML data management and preprocessing.
• Real-Time Monitoring: Implementation of real-time monitoring and alerts for model performance and drift using Azure Monitor, Application Insights, and Log Analytics.
• Model Versioning and Governance: Setup of model version control, automated workflows for model retraining, and performance tracking through AML and Azure DevOps.
• Documentation & Knowledge Sharing: Creation of comprehensive documentation for MLOps pipelines and deployment processes to ensure seamless handover and transparency.
Required Skills & Experience:
• 3+ years of hands-on experience in MLOps, DevOps, or Cloud Engineering roles, with a strong emphasis on Azure environments.
• Expertise in Azure Machine Learning (AML), including model management, AutoML, model deployment, and experiment tracking.
• Experience in building and maintaining CI/CD pipelines using Azure DevOps and version control systems like Git.
• Strong knowledge of Azure Kubernetes Service (AKS), Azure Container Instances (ACI), and Azure Functions for containerised model deployment.
• Proven experience with Azure Data Factory, Azure Databricks, and Azure Synapse Analytics for managing and orchestrating data pipelines.
• Familiarity with Terraform for infrastructure as code (IaC) and automated provisioning of Azure resources.
• Proficient in Python, with experience in scikit-learn, TensorFlow, PyTorch, or XGBoost for machine learning tasks.
• Solid understanding of model monitoring and logging using Azure Monitor, Application Insights, and Log Analytics.
• Knowledge of best practices for model governance, versioning, and deployment in a regulated FinTech environment.
Nice to Have:
• Azure certifications (e.g., Azure AI Engineer, Azure Solutions Architect).
• Experience with MLflow, Kubeflow, or other model management frameworks.
• Familiarity with containerisation and orchestration tools like Docker, Kubernetes, and Helm.
• Experience in data security and compliance standards relevant to the FinTech industry, including PCI DSS or SOC 2 compliance.
