Machine Learning Engineer with MLOps and Azure Databricks Expertise

MLE/MLOps, OOPs Python, Databricks, Azure Primary skills:Technology->Data Science->Machine Learning,Technology->Machine Learning->Python 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) 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 MLE/MLOps, OOPs Python, Databricks, Azure Primary skills:Technology->Data Science->Machine Learning,Technology->Machine Learning->Python 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) 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