MLE/MLOps, OOPs Python, Databricks, Azure

Key Responsibilities

Machine Learning Engineering

Develop, train, evaluate, and deploy machine learning models at scale Implement end-to-end ML pipelines from data ingestion to model serving Work on model optimization, validation, and performance monitoring Apply best practices for feature engineering and model lifecycle management

MLOps & Deployment

Build and maintain MLOps pipelines for CI/CD/CT (Continuous Training) Automate model deployment, versioning, and monitoring Implement experiment tracking and model registry (MLflow preferred) Ensure model reproducibility, scalability, and governance

Python (OOPs) Development

Develop modular, reusable, and scalable code using object-oriented Python Build robust backend services and ML utilities Write clean, testable, and well-documented code

Databricks

Develop and optimize workflows on Azure Databricks Work with PySpark for data processing and feature engineering Manage notebooks, jobs, clusters, and Delta Lake pipelines Optimize Spark jobs for performance and cost

Azure Cloud

Work with Azure services like Azure ML, Data Factory, Blob Storage, ADLS, Key Vault Deploy models and pipelines using Azure DevOps / CI-CD pipelines Implement secure, scalable, and cost-efficient cloud architectures

Data Engineering & Integration

Build and maintain data pipelines for ML workflows Integrate models with APIs and downstream applications Work with large datasets (structured & unstructured)