Ganesan Kasi
Azure Ai Platform Engineer @Tata Consultancy Services
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WORK HISTORY
Azure Ai Platform Engineer @Tata Consultancy Services
IN
Azure AI & ML Platform Engineer with 2 years of hands-on experience designing and managing Azure Machine Learning platforms in enterprise environments. Experienced in provisioning and configuring Azure ML workspaces, compute clusters, model deployment endpoints, networking integrations (VNet, Private Endpoints), and secure access using RBAC and managed identities.Skilled in building scalable ML infrastructure using Infrastructure as Code (Terraform), enabling standardized and repeatable environment deployments across Dev, Test, and Production. Implemented CI/CD pipelines for ML workflows using Azure DevOps and GitHub Actions to automate environment setup, model packaging, and deployment processes.Experienced in containerization with Docker and orchestration using Azure Kubernetes Service (AKS) to support real-time and batch inference workloads. Focused on platform reliability, security, governance, monitoring integration, and cost optimization to deliver production-ready Azure AI environments for data science and ML teams.
EDUCATION
Sri Venkateshwara polytechnic College
Diploma in computer engineering, Computer Science
ABOUT GANESAN KASI
I am an Azure AI Platform Engineer with 9+ years of experience in cloud, DevOps, and enterprise infrastructure, currently focused on designing and operationalizing secure, scalable AI-native platforms on Microsoft Azure.Over the past two years, I have specialized in building and managing Azure AI ecosystems integrating Azure Machine Learning, Azure OpenAI, Azure Databricks, Azure AI Search, and Azure Kubernetes Service (AKS). My work centers on enabling end-to-end AI workflows — from data ingestion and feature engineering to model lifecycle management, inference deployment, and governance.I design AI-ready Azure Landing Zones with secure networking (VNet, Private Endpoints), identity-based access controls, and policy-driven governance to support production-grade AI deployments across Dev, Test, and Production environments. I implement MLOps practices including MLflow-based experiment tracking, model registry management, CI/CD automation, and versioned model rollout strategies.I have experience deploying GPU-enabled workloads on AKS for real-time and batch inference, implementing Retrieval-Augmented Generation (RAG) architectures using vector search, and integrating Azure OpenAI for enterprise AI use cases.My focus areas include:• AI Platform Architecture• MLOps & Model Lifecycle Automation• Kubernetes-based AI Infrastructure• Responsible AI & Governance• Cost Optimization (FinOps) for AI WorkloadsWith a strong DevOps and infrastructure foundation, I bridge the gap between data science, engineering, and cloud operations — ensuring AI solutions are secure, scalable, and production-ready.
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