Job Title: Senior GCP Platform Engineer
Location: Woodland Hills, CA(Remote)
Duration: 24 Months + Extension
Bill Rate: $87/hour (C2C)
Job Type: W-2, 1099, C2C
Client: To Be Discussed Later
Work Authorization: US Citizen, Green Card, OPT-EAD, CPT, H-1B, H4-EAD, Other

Job Description:

We are looking for an experienced Senior GCP Platform Engineer to design, automate, secure, and manage enterprise-grade cloud infrastructure supporting core business applications and Generative AI initiatives.
The ideal candidate should have strong hands-on experience with Google Cloud Platform, Terraform, cloud networking, infrastructure automation, security, and platform engineering. The candidate will be responsible for building scalable GCP Landing Zones, implementing advanced networking architectures, developing reusable Terraform modules, and integrating Google Cloud AI services such as Vertex AI, Agent Builder, and Gemini APIs into enterprise environments.

Roles & Responsibilities:

Design, deploy, and manage enterprise-grade GCP Landing Zones.
Implement hierarchical resource management, including organizations, folders, projects, and resource governance.
Configure and manage Shared VPCs and multi-tenant cloud infrastructure with strong security boundaries.
Design and implement advanced GCP networking solutions, including VPC Peering, Private Service Connect (PSC), and Cloud Interconnect.
Implement VPC Service Controls to secure sensitive workloads and prevent unauthorized data access and exfiltration.
Develop, maintain, and optimize Infrastructure as Code (IaC) using Terraform.
Build reusable and modular Terraform templates for scalable infrastructure provisioning.
Manage Terraform remote state, state locking, versioning, and deployment workflows.
Develop and maintain automated CI/CD pipelines for infrastructure provisioning and deployment.
Integrate GCP infrastructure with enterprise applications and cloud-native services.
Configure and operationalize Vertex AI environments, including Vertex AI Endpoints, Custom Training, and Vector Search.
Integrate Generative AI and Agentic AI capabilities using Google Agent Builder and Gemini APIs.
Implement secure model execution environments within enterprise cloud security perimeters.
Build reusable infrastructure modules and standardized deployment patterns to enable developer self-service.
Establish compliant, production-ready infrastructure provisioning workflows for engineering and data science teams.
Implement least-privilege IAM policies, custom roles, and service account access controls.
Manage secrets, encryption keys, and sensitive configurations using Google Secret Manager and Cloud KMS.
Implement cloud security, governance, compliance, and perimeter protection controls.
Collaborate with application, infrastructure, data engineering, and AI/ML teams to deliver scalable cloud platforms.
Troubleshoot infrastructure, networking, security, Terraform, and deployment-related issues.

Required Technical Skills:

8–10 years of experience in cloud engineering, infrastructure engineering, or platform engineering.
Strong hands-on experience with Google Cloud Platform (GCP).
Strong experience designing and implementing GCP Landing Zones.
Experience with GCP Organization, Folder, Project, and Resource Hierarchy management.
Hands-on experience with Shared VPC and multi-project architecture.
Strong knowledge of GCP networking, including VPC, VPC Peering, Private Service Connect (PSC), and Cloud Interconnect.
Experience implementing GCP VPC Service Controls and enterprise network security.
Strong hands-on experience with Terraform and Infrastructure as Code (IaC).
Experience developing reusable, modular Terraform code and infrastructure templates.
Knowledge of Terraform remote state management and state locking.
Experience with CI/CD pipeline automation for infrastructure deployment.
Hands-on experience with Vertex AI, including Endpoints, Custom Training, and Vector Search.
Knowledge of Generative AI and Agentic AI integration within GCP.
Experience with Google Agent Builder and Gemini APIs.
Strong understanding of IAM, least-privilege access, service accounts, and custom roles.
Experience with Google Secret Manager and Cloud KMS.
Strong knowledge of cloud security, governance, compliance, and enterprise architecture.
Experience building standardized infrastructure modules and developer self-service platforms.
Strong troubleshooting, problem-solving, and communication skills.

Preferred / Desirable Skills:

Experience with AWS cloud infrastructure and AWS Cloud Engineering.
Exposure to multi-cloud and hybrid cloud architectures.
Experience with MLOps and AI platform engineering.
Knowledge of secure Generative AI deployment and enterprise AI governance.
Experience supporting large-scale enterprise cloud environments.
Knowledge of automated infrastructure testing and policy-as-code practices.