Google Cloud DevOps Engineer (GCP DevOps Engineer)

As a DevOps Engineer in this role, you will be responsible for designing, implementing, and managing end-to-end application DevOps pipelines. Your key responsibilities will include: - Building and maintaining CI/CD pipelines using Azure DevOps for automated deployments and releases - Managing Development, Staging, and Production environments with rollback and release strategies - Deploying, monitoring, and scaling applications on Google Cloud Platform (GCP) - Containerizing applications using Docker and orchestrating workloads using Kubernetes (GKE preferred) - Implementing Infrastructure as Code (IaC) using Terraform - Managing IAM roles, permissions, and cloud security best practices in GCP - Configuring and managing API gateways, rate limiting, logging, and monitoring solutions - Deploying and managing AI agents and AI-powered applications in production environments - Supporting AI/ML pipelines including LLM configuration, model deployment, and inference workflows - Configuring and managing Google AI Studio environments - Monitoring infrastructure and application performance to ensure high availability and reliability - Troubleshooting deployment, infrastructure, and production issues proactively - Collaborating with development and AI/ML teams for seamless delivery and integration - Supporting distributed systems and cloud-native architecture initiatives Qualifications Required: - 3+ years of hands-on experience in Google Cloud Platform (GCP) DevOps environments - Strong 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 - Valuable communication and collaboration abilities In addition, this company prefers candidates with experience in AI/ML production environments, familiarity with cloud-native DevOps practices, exposure to scalable microservices architecture, and experience handling large-scale production workloads. Immediate joiners are preferred for this position. As a DevOps Engineer in this role, you will be responsible for designing, implementing, and managing end-to-end application DevOps pipelines. Your key responsibilities will include: - Building and maintaining CI/CD pipelines using Azure DevOps for automated deployments and releases - Managing Development, Staging, and Production environments with rollback and release strategies - Deploying, monitoring, and scaling applications on Google Cloud Platform (GCP) - Containerizing applications using Docker and orchestrating workloads using Kubernetes (GKE preferred) - Implementing Infrastructure as Code (IaC) using Terraform - Managing IAM roles, permissions, and cloud security best practices in GCP - Configuring and managing API gateways, rate limiting, logging, and monitoring solutions - Deploying and managing AI agents and AI-powered applications in production environments - Supporting AI/ML pipelines including LLM configuration, model deployment, and inference workflows - Configuring and managing Google AI Studio environments - Monitoring infrastructure and application performance to ensure high availability and reliability - Troubleshooting deployment, infrastructure, and production issues proactively - Collaborating with development and AI/ML teams for seamless delivery and integration - Supporting distributed systems and cloud-native architecture initiatives Qualifications Required: - 3+ years of hands-on experience in Google Cloud Platform (GCP) DevOps environments - Strong 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 - Valuable communication and collaboration abilities In addition