MLE/MLOps, OOPs Python, Databricks, Azure Professional

Educational Requirements

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

Service Line

Data Analytics Unit

Responsibilities

• 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

Additional Responsibilities

• 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)

Technical and Professional Requirements

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

Preferred Skills

• Technology- >AI-Data science- >PYTHON
• Technology- >AI-Data science- >Machine Learning