Google Cloud DevOps Engineer (GCP DevOps Engineer) (Chennai)

Key Responsibilities: - Design, implement, and manage end-to-end application DevOps pipelines - Build and maintain CI/CD pipelines using Azure DevOps for automated deployments and releases - Manage Development, Staging, and Production environments with rollback and release strategies - Deploy, monitor, and scale applications on Google Cloud Platform (GCP) - Containerize applications using Docker and orchestrate workloads using Kubernetes (GKE preferred) - Implement Infrastructure as Code (IaC) using Terraform - Manage IAM roles, permissions, and cloud security best practices in GCP - Configure and manage API gateways, rate limiting, logging, and monitoring solutions - Deploy and manage AI agents and AI-powered applications in production environments - Support AI/ML pipelines including LLM configuration, model deployment, and inference workflows - Configure and manage Google AI Studio environments - Monitor infrastructure and application performance to ensure high availability and reliability - Troubleshoot deployment, infrastructure, and production issues proactively - Collaborate with development and AI/ML teams for seamless delivery and integration - Support distributed systems and cloud-native architecture initiatives Required Skills & Qualifications: - 3+ years of hands-on experience in Google Cloud Platform (GCP) DevOps environments - Solid experience managing live production deployments and cloud infrastructure in GCP - Expertise in Azure DevOps and CI/CD pipeline implementation - Strong knowledge of Docker and Kubernetes (GKE preferred) - Hands-on experience with Terraform and Infrastructure as Code practices - Experience managing cloud networking, IAM, security policies, and access controls - Knowledge of monitoring, logging, and observability tools - Experience configuring API gateways, rate limiting, and cloud-native services - Hands-on exposure to AI agents, AI/ML pipelines, and model deployment workflows - Experience working with Google AI Studio and LLM configurations - Strong troubleshooting, analytical, and problem-solving skills - Understanding of distributed systems and high-availability architecture - Good communication and collaboration abilities Preferred Skills: - Experience with AI/ML production environments and inference pipelines - Familiarity with cloud-native DevOps practices and automation - Exposure to scalable microservices architecture - Experience handling large-scale production workloads - Immediate joiners preferred Key Responsibilities: - Design, implement, and manage end-to-end application DevOps pipelines - Build and maintain CI/CD pipelines using Azure DevOps for automated deployments and releases - Manage Development, Staging, and Production environments with rollback and release strategies - Deploy, monitor, and scale applications on Google Cloud Platform (GCP) - Containerize applications using Docker and orchestrate workloads using Kubernetes (GKE preferred) - Implement Infrastructure as Code (IaC) using Terraform - Manage IAM roles, permissions, and cloud security best practices in GCP - Configure and manage API gateways, rate limiting, logging, and monitoring solutions - Deploy and manage AI agents and AI-powered applications in production environments - Support AI/ML pipelines including LLM configuration, model deployment, and inference workflows - Configure and manage Google AI Studio environments - Monitor infrastructure and application performance to ensure high availability and reliability - Troubleshoot deployment, infrastructure, and production issues proactively - Collaborate with development and AI/ML teams for seamless delivery and integration - Support distributed systems and cloud-native architecture initiatives Required Skills & Qualifications: - 3+ years of hands-on experience in Google Cloud Platform (GCP) DevOps environments - Solid experience managing live production deployments and cloud infrastructure in GCP - Expertise in Azure DevOps and CI/CD pipeline implementation - Strong knowledge of Docker and Kubernetes (GKE preferred) - Hands-on experience with Terraform and Infrastructure as Code practices - Experience managing cloud networking, IAM, security policies, and access controls - Knowledge of monitoring, logging, and observability tools - Experience configuring API gateways, rate limiting, and cloud-native services - Hands-on exposure to AI agents, AI/ML pipelines, and model deployment workflows - Experience working with Google AI Studio and LLM configurations - Strong troubleshooting, analytical, and problem-solving skills - Understanding of distributed systems and high-availability architecture - Good communication and collaboration abilities Preferred Skills: - Experience with AI/ML production environments and inference pipelines - Familiar