Job Title

We are looking for a skilled and proactive Azure MLOps Engineer with strong expertise in Databricks to join our AI & Data Engineering team. This is a permanent role where you'll be responsible for building scalable and automated MLOps workflows to support end-to-end machine learning deployments on Azure.Responsibilities -Design and implement robust MLOps pipelines using Azure Machine Learning and Azure DevOps. -Collaborate with Data Scientists to operationalize ML models and manage their lifecycle using Azure ML services. -Leverage Databricks (PySpark, MLflow, Delta Lake) to build and scale model training and data pipelines.- Automate workflows for model training, testing, deployment, and monitoring using Azure CI/CD tools.- Deploy and monitor models in production environments with proper logging, alerting, and version control.- Maintain and manage infrastructure using Terraform, ARM templates, or Bicep for consistency and repeatability.- Ensure governance, auditability, and security standards are met across ML assets.- Work with DevOps and Data Engineering teams to optimize pipelines for performance and cost.Requirements:- Hands-on experience with Azure Machine Learning (Pipelines, Model Registry, Endpoints).- Strong knowledge of Azure Databricks and experience building workflows using PySpark and MLflow.- Deep understanding of MLOps principles, automation, and model lifecycle management.- Proficiency in Python and experience scripting for ML pipeline automation.- Experience with Azure DevOps for building CI/CD pipelines in ML or data environments.- Exposure to containerization (Docker) and deploying ML models in production.- Familiarity with monitoring and logging tools like Azure Monitor, Application Insights, or custom logging setups.- Solid understanding of data engineering concepts, feature pipelines, and orchestration.Qualifications - Experience with Terraform or Bicep for infrastructure as code.- Knowledge of AKS (Azure Kubernetes Service) for containerized model deployment.- Exposure to real-time inference, model explainability, and drift detection solutions.- Prior experience working in insurance, finance, or healthcare domains is a plus.