01Overview
Educational Requirements
- Bachelor of Engineering, BTech, BSc, BCA, MSc, MTech, MCA
Service Line
Data Analytics Unit
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-efficient 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)
Technical and Professional Requirements Core Skills
- 35 years of experience in Machine Learning / MLOps
- Solid 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
Technology Preferences
- Python
- Azure NAT Gateway
- Databricks
- Databricks Machine Learning .
