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

• 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)