Azure Machine Learning - S

Tata Consultancy Services; Chennai District; On-site; INR 1,500,000 - 2,300,000; Full time

Job summary

Tata Consultancy Services in Chennai is seeking an experienced ML Engineer to design, develop, and deploy ML models using Azure ML and AKS. You will build end-to-end ML pipelines, ensure governance, and collaborate with data scientists to operationalize models.

Applicants should have 3+ years in ML/Data Science, strong Python skills, and hands-on experience with Azure ML, TensorFlow, PyTorch, and CI/CD for ML workflows.

Qualifications

• 3+ years of experience in ML, data science, or AI solution development.
• Hands-on with Azure Machine Learning (Azure ML).
• Strong Python programming and ML framework knowledge (Scikit-learn, TensorFlow, PyTorch, XGBoost).
• Experience with REST APIs and integrating ML into applications.
• Familiarity with data processing and ETL concepts.

Responsibilities

• Design, develop, and deploy ML models and AI solutions using Azure services.
• Build end-to-end ML pipelines for data prep, training, validation, deployment, and monitoring.
• Develop and manage MLOps practices, including model versioning and CI/CD processes.
• Collaborate with data scientists to operationalize models.
• Deploy and manage models using Azure ML and AKS.
• Monitor model performance and implement retraining strategies.
• Optimize ML solutions for scalability, performance, and cost efficiency.
• Integrate AI/ML services into enterprise applications and processes.
• Ensure compliance with data governance, security, and regulatory requirements.
• Document designs, workflows, and deployment procedures.

Skills

Python

ML

Data Science

Azure ML

REST APIs

ETL concepts

Feature engineering

Education

Bachelor's or Master's in CS/DS/AI/Engineering

Tools

Docker

Kubernetes

Terraform

Azure DevOps

GitHub Actions

AKS

Azure Data Factory

Azure Databricks

Azure Synapse

Azure Monitor

ACR

Azure Blob Storage

Azure Data Lake Storage

Job description

Key Responsibilities

• Design, develop, and deploy machine learning models and AI solutions using Azure services.
• Build and maintain end-to-end ML pipelines for data preparation, training, validation, deployment, and monitoring.
• Develop and manage MLOps practices, including model versioning, automation, and CI/CD processes.
• Collaborate with data scientists to operationalize machine learning models.
• Deploy and manage models using Azure Machine Learning and Azure Kubernetes Service (AKS).
• Monitor model performance and implement retraining strategies as needed.
• Optimize machine learning solutions for scalability, performance, and cost-efficiency.
• Integrate AI/ML services into enterprise applications and business processes.
• Ensure compliance with data governance, security, and regulatory requirements.
• Document technical designs, workflows, and deployment procedures.

Required Skills & Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Engineering, or a related field.
• 3+ years of experience in Machine Learning, Data Science, or AI solution development.
• Hands-on experience with Azure Machine Learning (Azure ML).
• Strong programming skills in Python.
• Experience with machine learning frameworks such as:
• Scikit-learn
• TensorFlow
• PyTorch
• XGBoost
• Knowledge of supervised and unsupervised learning techniques.
• Experience in feature engineering, model evaluation, and model deployment.
• Familiarity with REST APIs and integrating ML services into applications.
• Strong understanding of data processing and ETL concepts.

Azure Skills

• Azure Machine Learning Studio
• Azure AI Services / Azure Cognitive Services
• Azure OpenAI (preferred)
• Azure Data Factory
• Azure Databricks
• Azure Synapse Analytics
• Azure Blob Storage
• Azure Data Lake Storage
• Azure Kubernetes Service (AKS)
• Azure Container Registry (ACR)
• Azure Key Vault
• Azure Monitor

MLOps & DevOps Skills

• CI/CD implementation using Azure DevOps or GitHub Actions
• ML model lifecycle management
• Model registry and version control
• Containerization using Docker
• Kubernetes orchestration
• Infrastructure as Code (Terraform/Bicep preferred)