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

📌 MLE/MLOps, OOPs Python, Databricks, Azure Professional (Bengaluru)
🏢 Infosys
📍 Bengaluru