MLE/MLOps, OOPs Python, Databricks, Azure

Key Responsibilities:

• 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

Technical Requirements:

• Required Skills Qualifications
• Core Skills
• 3 5 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

Preferred Skills:

Technology->AI-Data science->Databricks Machine Learning,Technology->AI-Data science->PYTHON,Technology->Data Engineering->Databricks,Technology->Cloud Platform->Azure Networking Services->Azure NAT Gateway