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