- Machine Learning Engineering
- Design develop and deploy scalable ML models and AI solutions
- Build end to end pipelines covering data ingestion feature engineering model training evaluation and deployment
- Apply advanced techniques for model optimization validation and explainability
- Ensure models are production ready with high accuracy and performance
- MLOps Lifecycle Management
- Design and implement MLOps frameworks for CI CD CT continuous training
- Automate model deployment versioning monitoring and rollback strategies
- Implement model performance tracking drift detection and alerting systems
- Use tools like MLflow for experiment tracking and model registry
- Python OOPs Development
- Write scalable modular and reusable code using object oriented Python
- Develop APIs and backend services for model serving and integration
- Implement best practices for code quality testing and maintainability
- Databricks Big Data
- Build and optimize pipelines using Azure Databricks and PySpark
- Work with Delta Lake for data versioning and reliability
- Manage Databricks clusters jobs and workflows
- Optimize Spark jobs for performance scalability and cost efficiency
- Azure Cloud Platform
- Design ML solutions using Azure services Azure ML ADLS Data Factory Key Vault Synapse
- Implement secure and scalable cloud architectures
- Integrate ML pipelines with Azure DevOps CI CD pipelines
- Ensure compliance with data governance and security policies
- Data Engineering Integration
- Develop robust data pipelines for ML workflows
- Handle large scale structured and unstructured datasets
- Integrate ML models with downstream applications via APIs microservices
- Preferred Skills
- Experience with feature stores and model monitoring tools
- Knowledge of Docker Kubernetes containerization
- Familiarity with streaming Kafka Event Hub
- Experience with Lakehouse architecture Delta Lake
- Exposure to GenAI LLMOps optional added advantage