Google Cloud DevOps Engineer (GCP DevOps Engineer)

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 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 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