The GCP Architect Engineer is responsible for designing, building, and operating secure, scalable, and compliant cloud and AI-enabled platforms on Google Cloud Platform (GCP).
This role focuses on enabling application, data, and AI teams through standardized cloud infrastructure, Kubernetes platforms, and approved Google AI services. The engineer ensures that AI and cloud workloads are deployed with strong governance, security controls, observability, and enterprise reliability standards.
Working cross-functionally with Cloud, Data & AI, Security, and Risk teams, this role supports the safe and compliant adoption of AI capabilities within a regulated enterprise environment.
5+ years of experience in cloud, platform, or DevOps engineering.
Strong hands-on experience with Google Cloud Platform.
Expertise in Terraform and Infrastructure as Code.
Experience operating Kubernetes (GKE) in enterprise environments.
Proficiency in scripting (Python, Bash, or Go).
Strong understanding of cloud security, IAM, and networking.
Experience working in regulated or highly governed environments.
Enterprise Cloud & AI Platform Engineering
Design and maintain enterprise GCP landing zones aligned with governance and security standards.
Build and operate shared cloud services supporting both AI and non-AI workloads.
Implement Infrastructure as Code (Terraform) for platform, networking, and AI service enablement.
Enable secure hybrid connectivity and controlled data access patterns.
Kubernetes & AI Workload Enablement
Engineer and operate GKE clusters for application and AI inference workloads.
Standardize containerized deployment patterns using approved base images.
Support GPU-enabled workloads where required.
Enable scalable deployment of AI APIs and services.
Google AI & GenAI Platform Services
Enable and operate approved Google AI services, including:
Vertex AI (model hosting, endpoints, pipelines – platform enablement)
Gemini APIs and other managed GenAI services
BigQuery ML and AI-integrated analytics platforms
Implement secure access controls, networking, and monitoring for AI services.
Integrate AI services into CI/CD pipelines and enterprise SDLC processes.
Partner with Data & AI teams to operationalize AI workloads safely and compliantly.
DevOps & MLOps Foundations
Build secure CI/CD pipelines for cloud and AI workloads.
Support MLOps capabilities including:
Automated model deployment and promotion
Environment lifecycle management
Monitoring and logging for AI endpoints
Enforce policy-as-code, guardrails, and approval workflows for AI usage.
Security, Compliance & Governance
Implement IAM, workload identity, and least-privilege access models.
Enforce encryption, data residency, and secure networking practices.
Integrate platform telemetry with enterprise logging and monitoring systems.
Support audit, risk, and regulatory compliance requirements.
Reliability, Observability & Cost Optimization
Design highly available and resilient cloud and AI platforms.
Monitor workload performance, reliability, and cost usage.
Optimize cloud and AI spend through budgets and usage controls.
Participate in incident response and root-cause analysis.
Collaboration & Standards
Partner with Cloud, Data & AI, Security, Risk, and Application teams.
Contribute to enterprise cloud and AI platform standards.
Document reference architectures and best practices.
Provide guidance on responsible AI platform adoption.
Preferred Qualifications (AI-Focused)
Experience enabling or operating Google AI services such as:
Vertex AI (endpoints, pipelines, monitoring)
Gemini APIs or other managed GenAI services
BigQuery ML
Familiarity with MLOps concepts (model deployment, versioning, monitoring).
Experience supporting AI inference workloads.
Understanding of responsible AI practices, data governance, and model risk controls.
GCP certifications (Cloud Architect, DevOps Engineer; AI certifications a plus).
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