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