Key Responsibilities
Design, develop, and deploy AI/ML solutions using Microsoft Azure AI services.
Build and manage end-to-end MLOps pipelines for model training, validation, deployment, and monitoring.
Implement CI/CD pipelines using Azure DevOps, GitHub Actions, or similar tools.
Automate infrastructure provisioning using Infrastructure as Code (IaC) tools such as Terraform, ARM Templates, or Bicep.
Deploy and manage AI workloads on Azure Kubernetes Service (AKS), Azure Container Apps, and Azure Machine Learning.
Integrate Azure OpenAI, Cognitive Services, Azure AI Search, and other Azure AI capabilities into enterprise applications.
Monitor application performance, model accuracy, and infrastructure health using Azure Monitor, Log Analytics, and Application Insights.
Ensure security, compliance, governance, and cost optimization across Azure settings.
Collaborate with cross-functional teams to translate business requirements into scalable AI solutions.
Troubleshoot deployment, performance,



and operational issues across cloud and AI environments.

Required Skills & Qualifications
Robust experience with Microsoft Azure cloud platform.
Hands-on experience with:
Azure Machine Learning
Azure OpenAI Service
Azure AI Search
Azure Cognitive Services
Azure Kubernetes Service (AKS)
Azure Functions
Azure Container Registry (ACR)
Azure Storage Services
Experience with Azure DevOps and CI/CD pipeline implementation.
Proficiency in Python, C#, or Java.
Knowledge of Docker and Kubernetes.
Experience with Infrastructure as Code (Terraform, Bicep, ARM Templates).
Familiarity with Git version control systems.
Understanding of MLOps concepts and AI model lifecycle management.
Knowledge of REST APIs and microservices architecture.
Experience with monitoring and observability tools.

📌 Azure Ai With Devops S Bengaluru
🏢 Tata Consultancy Services
📍 Bengaluru