Corporate Vice President - Google Cloud Platform Engineer

About the role

The GCP Platform Engineer at New York Life is responsible for designing, building, and operating secure, compliant, and scalable cloud and AI-enabled platforms on Google Cloud Platform (GCP).

Responsibilities

• Enterprise Cloud & AI Platform Design and maintenance
• Build and operate shared cloud services supporting AI and non-AI workloads on GCP components like Cloud Storage, Cloud Functions, Cloud Run, Cloud Pub/Sub, and Cloud Spanner.
• Implement Infrastructure as Code (Terraform) for platform, networking, and AI service enablement
• Support hybrid connectivity and secure data access patterns for AI use cases using Cloud Interconnect and Cloud VPN.
• Kubernetes, Containers & AI Workloads
• Enable containerized AI services and microservices using approved base images from Google Container Registry (GCR) or JFrog Artifact Registry.
• Support GPU-enabled workloads where approved.
• Implement standardized deployment patterns for AI APIs and services using Helm for Kubernetes deployment management
• Google AI / GenAI Enablement
• Enable and operate approved Google AI services, including: Vertex AI (model hosting, endpoints, pipelines – platform enablement only, agentic AI deployments and communication protocols in Vertex AI Agent Builder and Agent Engine), Gemini APIs and other managed GenAI services (as approved by NYL governance)
• BigQuery ML and AI-integrated analytics platforms
• Implement secure access controls, networking, and monitoring for AI services using Cloud Identity & Access Management (IAM), VPC Service Controls, and Cloud Monitoring.
• Integrate AI platforms with CI/CD pipelines and enterprise SDLC controls using tools like Harness CICD
• Collaboration & Governance
• Partner with Data & AI, InfoSec, Security, Risk, and Application teams to ensure secure, compliant, and efficient AI platform usage.
• Contribute to enterprise standards for cloud and AI platform usage including Best Practices for GCP and Google Cloud Architecture Framework.
• Provide guidance on responsible AI platform adoption using frameworks like Google's AI Principles and Fairness Indicators.
• Document reference architectures and best practices for GCP AI services, MLOps, and cloud infrastructure.

Qualifications

• 5+ years of experience in cloud, platform, or DevOps engineering
• Strong hands-on experience with Google Cloud Platform specifically services like GKE, BigQuery, Cloud Storage, Cloud Functions, and Vertex AI
• Expertise in Terraform and Infrastructure as Code
• Experience operating Kubernetes / GKE in enterprise environments with tools like kubectl, Helm
• Proficiency in scripting with languages like Python, Bash, or Go
• Strong understanding of cloud security, IAM, and networking using VPC, Cloud IAM, and VPC Service Controls
• Experience working in regulated or highly governed environments

Preferred Qualifications (AI-Focused)

• Experience enabling or operating Google AI services, such as: Vertex AI (endpoints, pipelines, monitoring, agentic AI engine and communication protocols), Gemini APIs or other managed GenAI services, BigQuery ML and AI-integrated analytics platforms
• Familiarity with MLOps concepts (model deployment, versioning, monitoring) using Kubeflow, TensorFlow Extended (TFX), and Vertex AI Pipelines
• Experience supporting AI inference workloads (not necessarily model training) in GKE or Cloud Run
• Understanding of Responsible AI, data governance, and model risk controls
• GCP certifications like Google Cloud Certified – Professional Cloud Architect, Google Cloud Certified – Professional Cloud DevOps Engineer; AI-related certifications such as Google Cloud Certified – Professional Machine Learning Engineer are a plus