[Remote] VP - Platform Engineer - Google Cloud Platform DevOps
Note: The job is a remote job and is open to candidates in USA. StevenDouglas is a leading boutique Search and Interim Resources firm connecting top talent with clients across various industries. The VP Platform Engineer role focuses on designing, building, and operating secure, compliant, and scalable cloud and AI-enabled platforms on Google Cloud Platform, supporting application, data, and analytics teams while ensuring regulatory and security compliance.
Responsibilities
- Design and maintain enterprise Google Cloud Platform landing zones using Google Cloud Deployment Manager, Terraform, and Cloud Foundation Toolkit aligned with governance standards
- Build and operate shared cloud services supporting AI and non-AI workloads on Google Cloud Platform 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
- Engineer and operate GKE (Google Kubernetes Engine) clusters for application and AI inference 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
- Enable and operate approved Google AI services, including Vertex AI, Gemini APIs, and BigQuery ML
- Implement secure access controls, networking, and monitoring for AI services using Cloud IAM, VPC Service Controls, and Cloud Monitoring
- Integrate AI platforms with CI/CD pipelines and enterprise SDLC controls using tools like Harness CICD
- Partner with Data & AI teams to operationalize AI workloads safely and compliantly within Google Cloud environments
- Build secure CI/CD pipelines for application and AI workloads using Harness CI/CD
- Support MLOps foundations such as model deployment automation via Kubeflow, TensorFlow Extended (TFX), Vertex AI Pipelines, and Vertex AI Model Registry
- Environment promotion and rollback using Terraform
- Monitoring and logging for AI endpoints using New Relic, Cloud Logging, and Cloud Monitoring
- Enforce guardrails, approvals, and policy-as-code for AI usage with Cloud Security Command Center, Google Cloud Policy Analyzer, and Open Policy Agent (OPA)
- Implement IAM, workload identity, and least-privilege models for AI services using Cloud IAM and Workload Identity Federation
- Enforce data residency, encryption, and access policies using Cloud Key Management Service (KMS) and Cloud Data Loss Prevention (DLP)
- Integrate AI platform telemetry with enterprise logging, monitoring, and SIEM using Cloud Logging, Cloud Monitoring, and New Relic
- Support audits, risk reviews, and regulatory requirements by leveraging Google Cloud Security Command Center, Cloud Audit Logs, and Cloud Data Loss Prevention API
- Design platforms for high availability and resilience including AI services using GKE, Cloud Spanner, Cloud SQL, and Google Cloud Load Balancing
- Monitor AI workloads for performance, reliability, and cost usage using New Relic, Cloud Monitoring, Cloud Trace, and Harness CCM
- Optimize cloud and AI service costs using budgets and usage controls with Google Cloud Billing, Budgets, Alerts, and Harness CCM
- Participate in incident response and root-cause analysis logged in ServiceNow and manage incident notifications through PagerDuty
- 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 Google Cloud Platform 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 Google Cloud Platform AI services, MLOps, and cloud infrastructure
Skills
- 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
- 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
- Google Cloud Platform 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
Benefits
- Remote work arrangement for the New York, NY location
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