Azure ML

Job description

Key Responsibilities: • Design, build, and deploy end-to-end machine learning models using Azure Machine Learning services. • Develop and maintain MLOps pipelines for model training, testing, deployment, and monitoring. • Work with Azure Databricks, Azure Data Factory, Azure Synapse, and Azure Blob/Data Lake for data ingestion and processing. • Implement CI/CD pipelines for ML workflows using Azure DevOps, GitHub Actions, or similar tools. • Deploy ML models as REST APIs/endpoints using Azure ML managed endpoints, AKS, or containers. • Monitor model performance, drift, and retraining processes in production. • Collaborate with data scientists to operationalize ML models and optimize performance. • Ensure best practices in security, scalability, governance, and cost optimization in Azure. • Build reusable components for feature engineering, model training, and inference. • Troubleshoot production issues related to ML pipelines and deployed models. Required Skills: • 4--7 years of overall IT experience with strong exposure to Machine Learning and Azure Cloud. • Hands-on experience with Azure Machine Learning Studio / Azure ML Service. • Strong knowledge of Python , SQL, and ML libraries such as Scikit-learn, Pandas, NumPy. • Experience with MLOps frameworks, model versioning, and experiment tracking. • Knowledge of Docker, Kubernetes/AKS, and containerized deployments. • Experience in Azure DevOps, Git, and CI/CD pipeline implementation. • Familiarity with data pipelines and ETL workflows on Azure. • Understanding of model monitoring, retraining, and lifecycle management. • Good knowledge of supervised/unsupervised learning, feature engineering, and model evaluation techniques. • Strong problem-solving and communication skills. Preferred Skills: • Experience with Azure Databricks and PySpark. • Knowledge of LLMs, Generative AI, or Azure OpenAI integration is a plus. • Familiarity with Terraform/ARM templates/Bicep for infrastructure automation. • Experience with Power BI or reporting tools for ML insights visualization. • Azure certifications such as: • Microsoft Certified: Azure Data Scientist Associate • Microsoft Certified: Azure AI Engineer Associate • Microsoft Certified: Azure Solutions Architect Expert (nice to have)