ML Ops Engineer

As a skilled MLOps Support Engineer, you will be responsible for monitoring and managing ML model operational pipelines in AzureML and MLflow. Your primary focus will be on automation, integration validation, and CI/CD pipeline management to ensure stability and reliability in model deployment lifecycles. Your objectives in this role include supporting and monitoring MLOps pipelines in AzureML and MLflow, managing CI/CD pipelines for model deployment and updates, handling model registry processes, performing testing and validation of integrated endpoints, automating monitoring and upkeep of ML pipelines, as well as troubleshooting and resolving pipeline and integration-related issues. In your day-to-day responsibilities, you will support production ML pipelines using AzureML and MLflow, configure and manage model versioning and registry lifecycle, automate alerts, monitoring tasks, and routine pipeline operations, validate REST API endpoints for ML models, implement CI/CD workflows for ML deployments, document and troubleshoot operational issues related to ML services, and collaborate with data scientists and platform teams to ensure delivery continuity. To excel in this role, you should possess proficiency in AzureML, MLflow, and Databricks, have a strong command over Python, experience with Azure CLI and scripting, a good understanding of CI/CD practices in MLOps, knowledge of model registry management and deployment validation, and at least 3-5 years of relevant experience in MLOps environments. While not mandatory, it would be beneficial to have skills such as exposure to monitoring tools like Azure Monitor and Prometheus, experience with REST API testing tools such as Postman, and familiarity with Docker/Kubernetes in ML deployments.,