01Overview
Role & responsibilities
Machine Learning Engineering
Develop, train, evaluate, and deploy machine learning models at scale
Implement end-to-end ML pipelines from data ingestion to model serving
Work on model optimization, validation, and performance monitoring
Apply best practices for feature engineering and model lifecycle management
MLOps & Deployment
Build and maintain MLOps pipelines for CI/CD/CT (Continuous Training)
Automate model deployment, versioning, and monitoring
Implement experiment tracking and model registry (MLflow preferred)
Ensure model reproducibility, scalability, and governance
Python (OOPs) Development
Develop modular, reusable, and scalable code using object-oriented Python
Build robust backend services and ML utilities
Write clean, testable, and well-documented code
Databricks
Develop and optimize workflows on Azure Databricks
Work with PySpark for data processing and feature engineering
Manage notebooks, jobs, clusters, and Delta Lake pipelines
Optimize Spark jobs for performance and cost
Azure Cloud
Work with Azure services like Azure ML, Data Factory, Blob Storage, ADLS, Key Vault
Deploy models and pipelines using Azure DevOps / CI-CD pipelines
Implement secure, scalable, and cost-productive cloud architectures
Data Engineering & Integration
Build and maintain data pipelines for ML workflows
Integrate models with APIs and downstream applications
Work with large datasets (structured & unstructured)
Preferred candidate profile
Required Skills & Qualifications
Core Skills
35 years of experience in Machine Learning / MLOps
Strong proficiency in Python with OOP concepts (mandatory)
Hands-on experience with Databricks & PySpark
Solid experience with Azure cloud ecosystem
Technical Skills
Experience with ML frameworks (Scikit-learn, TensorFlow, PyTorch)
Hands-on with MLflow (experiment tracking & model registry)
Knowledge of CI/CD tools (Azure DevOps, Jenkins, GitHub Actions)
Strong understanding of data structures, algorithms, and system design basics
Experience with REST APIs and microservices
Preferred Skills
Exposure to feature stores and model monitoring tools
Knowledge of Docker & Kubernetes
Familiarity with Delta Lake, data lakes, and warehouse architectures
Experience with streaming (Kafka/Event Hub)
Understanding of data governance and security best practices .
