MLE/MLOps, OOPs Python, Databricks, Azure Professional

Educational Requirements

• Bachelor of Engineering, BTech, BSc, BCA, MSc, MTech, MCA

Service Line 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