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)

* Primary skills:Technology->Data Science->Machine Learning,Technology->Machine Learning->Python