MLE/MLOps, OOPs Python, Databricks, Azure Professional

Job description

Educational Requirements - Bachelor of Engineering, BTech, BSc, BCA, MSc, MTech, MCA Service Line Data Analytics Unit 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) Technical and Professional Requirements Core Skills - 35 years of experience in Machine Learning / MLOps - Strong proficiency in Python with OOP concepts (mandatory) - Hands-on experience with Databricks PySpark - Solid experience with Azure cloud ecosystem Technical Skills - Experience with ML frameworks (Scikit-learn, TensorFlow, PyTorch) - Hands-on with MLflow (experiment tracking model registry) - Knowledge of CI/CD tools (Azure DevOps, Jenkins, GitHub Actions) - Strong understanding of data structures, algorithms, and system design basics - Experience with REST APIs and microservices Preferred Skills - Exposure to feature stores and model monitoring tools - Knowledge of Docker Kubernetes - Familiarity with Delta Lake, data lakes, and warehouse architectures - Experience with streaming (Kafka/Event Hub) - Understanding of data governance and security best practices Technology Preferences - Python - Azure NAT Gateway - Databricks - Databricks Machine Learning