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