GCP Platform Engineer
Job Title: GCP Platform Engineer – Enterprise Cloud; AI Platform
Location: Lebanon NJ (Hybrid role)
Role Summary:
The GCP Platform Engineer is responsible for designing, building, and operating secure, compliant, and scalable cloud and AI-enabled platforms on Google Cloud Platform (GCP). This role enables application, data, and analytics teams by providing standardized cloud infrastructure, Kubernetes platforms, and approved Google AI services, while meeting financial services regulatory, security, and resiliency requirements.
The engineer partners with the Cloud, Data ; AI teams, Information Security, and Risk to ensure AI workloads are deployed with appropriate governance, data controls, and observability
Key Responsibilities
Enterprise Cloud ; AI Platform
Design and maintain enterprise GCP landing zones aligned with governance standards
Build and operate shared cloud services supporting AI and non-AI workloads
Implement Infrastructure as Code (Terraform) for platform, networking, and AI service enablement
Support hybrid connectivity and secure data access patterns for AI use cases Kubernetes, Containers ; AI Workloads
Engineer and operate GKE clusters for application and AI inference workloads Enable containerized AI services and microservices using approved base images Support GPU-enabled workloads where approved
Implement standardized deployment patterns for AI APIs and services Google AI / GenAI EnablementEnable and operate approved Google AI services, including:
Vertex AI (model hosting, endpoints, pipelines – platform enablement only, agentic AI engine and communication protocols)
Job Title: GCP Platform Engineer – Enterprise Cloud; AI Platform
Location: Lebanon NJ (Hybrid role)
Role Summary:
The GCP Platform Engineer is responsible for designing, building, and operating secure, compliant, and scalable cloud and AI-enabled platforms on Google Cloud Platform (GCP). This role enables application, data, and analytics teams by providing standardized cloud infrastructure, Kubernetes platforms, and approved Google AI services, while meeting financial services regulatory, security, and resiliency requirements.
The engineer partners with the Cloud, Data ; AI teams, Information Security, and Risk to ensure AI workloads are deployed with appropriate governance, data controls, and observability
Key Responsibilities
Enterprise Cloud ; AI Platform
Design and maintain enterprise GCP landing zones aligned with governance standards
Build and operate shared cloud services supporting AI and non-AI workloads
Implement Infrastructure as Code (Terraform) for platform, networking, and AI service enablement
Support hybrid connectivity and secure data access patterns for AI use cases Kubernetes, Containers ; AI Workloads
Engineer and operate GKE clusters for application and AI inference workloads Enable containerized AI services and microservices using approved base images Support GPU-enabled workloads where approved
Implement standardized deployment patterns for AI APIs and services Google AI / GenAI EnablementEnable and operate approved Google AI services, including:
Vertex AI (model hosting, endpoints, pipelines – platform enablement only, agentic AI engine and communication protocols)