Job Title: GCP Cloud Architect (Forward Deployed Engineering)
About the Role
We are looking for a hands-on GCP Cloud Architect / Forward Deployed Engineer to design, build, and operationalize enterprise-grade Google Cloud platforms. The role covers end-to-end ownership from Day 0 GCP setup through production deployment, Infrastructure as Code (IaC), SRE, observability, and Agentic AI platform enablement.
Key Responsibilities
- Design and implement enterprise GCP landing zones, IAM, VPC, Shared VPC, firewalls, Private Service Connect, and security controls.
- Build and manage infrastructure using Terraform (Terragrunt preferred) with 100% Infrastructure as Code.
- Establish SRE practices including SLIs, SLOs, SLAs, error budgets, incident management, disaster recovery, and automation.
- Implement monitoring and observability using Google Cloud Operations Suite, Cloud Monitoring, Cloud Logging, Prometheus, Grafana, OpenTelemetry, or Datadog.
- Design Multi-Zone/Multi-Region, Active-Active/Active-Passive architectures with backup, failover, business continuity, and RTO/RPO planning.
- Implement secure authentication and authorization using OAuth 2.0, OIDC, SAML, Zero Trust, IAM, API security, Secrets Manager, and KMS.
- Deploy AI infrastructure using Vertex AI, GKE, Cloud Run, vector databases, and orchestration frameworks.
- Build CI/CD pipelines using GitHub Actions, Cloud Build, or GitLab CI.
- Collaborate with engineering, product, and client teams to deliver scalable cloud solutions.
Required Qualifications
- 6-8 years of hands-on experience designing and operating enterprise workloads on Google Cloud Platform.
- GCP Professional Cloud Architect certification preferred.
- Experience delivering at least 3 enterprise-scale GCP production projects.
- Strong expertise in Terraform, GCP networking, IAM, security, and Infrastructure as Code.
- Hands-on experience with SRE, observability, distributed tracing, monitoring, logging, and alerting.
- Experience supporting AI/LLM infrastructure, including Vertex AI, GKE, Cloud Run, and model hosting.
- Strong scripting skills in Python, Bash, along with Docker and modern CI/CD tools.
Job Title: GCP Cloud Architect (Forward Deployed Engineering)
About the Role
We are looking for a hands-on GCP Cloud Architect / Forward Deployed Engineer to design, build, and operationalize enterprise-grade Google Cloud platforms. The role covers end-to-end ownership from Day 0 GCP setup through production deployment, Infrastructure as Code (IaC), SRE, observability, and Agentic AI platform enablement.
Key Responsibilities
- Design and implement enterprise GCP landing zones, IAM, VPC, Shared VPC, firewalls, Private Service Connect, and security controls.
- Build and manage infrastructure using Terraform (Terragrunt preferred) with 100% Infrastructure as Code.
- Establish SRE practices including SLIs, SLOs, SLAs, error budgets, incident management, disaster recovery, and automation.
- Implement monitoring and observability using Google Cloud Operations Suite, Cloud Monitoring, Cloud Logging, Prometheus, Grafana, OpenTelemetry, or Datadog.
- Design Multi-Zone/Multi-Region, Active-Active/Active-Passive architectures with backup, failover, business continuity, and RTO/RPO planning.
- Implement secure authentication and authorization using OAuth 2.0, OIDC, SAML, Zero Trust, IAM, API security, Secrets Manager, and KMS.
- Deploy AI infrastructure using Vertex AI, GKE, Cloud Run, vector databases, and orchestration frameworks.
- Build CI/CD pipelines using GitHub Actions, Cloud Build, or GitLab CI.
- Collaborate with engineering, product, and client teams to deliver scalable cloud solutions.
Required Qualifications
- 6-8 years of hands-on experience designing and operating enterprise workloads on Google Cloud Platform.
- GCP Professional Cloud Architect certification preferred.
- Experience delivering at least 3 enterprise-scale GCP production projects.
- Strong expertise in Terraform, GCP networking, IAM, security, and Infrastructure as Code.
- Hands-on experience with SRE, observability, distributed tracing, monitoring, logging, and alerting.
- Experience supporting AI/LLM infrastructure, including Vertex AI, GKE, Cloud Run, and model hosting.
- Strong scripting skills in Python, Bash, along with Docker and modern CI/CD tools.