GCP DevOps Engineer

Job Summary : We are seeking a GCP DevOps Engineer with 4–5 years of experience, specializing in AI/ML workloads and GPU-based infrastructure. The ideal candidate should have strong hands-on expertise in Google Cloud Platform (GCP), with the ability to design, deploy, optimize, and troubleshoot AI-driven architectures, including GPU/TPU environments. This role requires deep technical knowledge in DevOps practices, cloud automation, and high-performance computing (HPC), along with excellent communication skills to collaborate with cross-functional teams and clients Key Responsibilities

• Design, implement, and manage scalable cloud infrastructure on Google Cloud Platform (GCP).
• Build, deploy, and maintain Kubernetes-based environments using Google Kubernetes Engine (GKE).
• Manage and optimize GPU/TPU-based infrastructure for AI/ML workloads.
• Configure and administer Compute Engine, Cloud Storage, IAM, VPC, and networking services.
• Develop and maintain CI/CD pipelines for application and machine learning model deployments.
• Monitor cloud infrastructure using Cloud Monitoring, Cloud Logging, Prometheus, and Grafana.
• Implement Infrastructure as Code (IaC) and deployment automation wherever applicable.
• Manage cloud governance, organization policies, project hierarchy, security controls, and cost optimization strategies.
• Troubleshoot infrastructure, networking, Kubernetes, and GPU performance issues.
• Optimize cluster performance, workload scheduling, and cloud resource utilization.
• Ensure high availability, security, compliance, backup, and disaster recovery for cloud environments.
• Collaborate with AI/ML Engineers and Data Scientists to deploy, scale, and monitor production machine learning models.
• Create technical documentation, operational runbooks, and architecture diagrams.
• Provide technical support during production incidents and client engagements.

Required Skills

• Strong hands-on experience with

Google Cloud Platform (GCP) , including cloud governance, organization policies, IAM, project hierarchy, and cost optimization.

• Expertise in

Google Kubernetes Engine (GKE) for deploying, managing, and scaling containerized applications in production environments.

• Proficiency in

Docker, Kubernetes, and Helm for containerization, orchestration, and application deployment automation.

• Experience managing

GPU/TPU-based infrastructure , including workload scheduling, resource allocation, performance tuning, and cost optimization for AI/ML applications.

• Strong understanding of

Compute Engine, Cloud Storage, BigQuery, Vertex AI/AI Platform , and other core GCP services.

• Hands-on experience building and maintaining

CI/CD pipelines for cloud-native applications and machine learning model deployments.

• Knowledge of

Cloud Monitoring, Cloud Logging, Prometheus, and Grafana to monitor infrastructure health, troubleshoot issues, and improve system reliability.

• Strong Linux administration skills, including system configuration, troubleshooting, scripting, and performance optimization.
• Experience implementing

cloud security best practices , Identity & Access Management (IAM), Security Command Center, and compliance controls.

• Ability to troubleshoot

Kubernetes clusters, networking issues, storage performance, and GPU utilization bottlenecks in production environments.

• Experience collaborating with

Data Scientists and AI/ML Engineers to deploy, scale, and manage production-ready machine learning workloads.

• Strong understanding of

high availability, disaster recovery, infrastructure automation, and cloud cost management in enterprise environments.

Qualifications:

• Bachelor's degree in Computer Science, Information Technology, or a related field
• 4–5 years of experience in DevOps / Cloud Engineering
• Hands-on experience with Google Cloud Platform (GCP)
• Experience supporting AI/ML workloads and GPU/accelerator-based infrastructure

Preferred Certification:

• Google Cloud Professional DevOps Engineer (Preferred)