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 environments.
- 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