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.
Robust 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 visualizat

📌 Azure ML (Kolkata)
🏢 PwC India
📍 Kolkata