Job Title: GCP Cloud Engineer-DLTJP00063939
Location: Fort Lauderdale, FL/Irving, TX/NJ/NY(local only)
Required Experience: Relevant experience in GCP cloud engineering, Terraform, Infrastructure as Code (IaC), and cloud automation
Technical Focus: GCP, Terraform, CI/CD, Cloud Networking, Data Platforms, and AI/ML Infrastructure
Job Summary
We are seeking an experienced GCP Cloud Engineer to design, build, automate, and maintain secure, scalable, and reliable cloud infrastructure on Google Cloud Platform. The ideal candidate will have strong expertise in Terraform-based Infrastructure as Code (IaC), cloud provisioning, CI/CD automation, GCP networking, identity and access management, and cloud-native services.
The candidate will collaborate with Data Engineering and AI/ML teams to support enterprise data platforms, APIs, and AI-driven workloads, including infrastructure for Vertex AI Agent Engine, Vertex AI Search, and Model Armor. This role requires hands-on experience building cloud environments from scratch, implementing automation best practices, maintaining production reliability, and enforcing security and governance standards.
Key Responsibilities
- Design, develop, and maintain reusable Terraform modules to provision and manage enterprise-grade GCP infrastructure across development, testing, staging, and production environments.
- Build complete GCP cloud environments from scratch using Infrastructure as Code, automation frameworks, and standardized deployment practices.
- Design, deploy, and manage cloud-native solutions using GCP services, including Cloud Run, BigQuery, Cloud Storage, Firestore, IAM, Secret Manager, and Cloud Load Balancing.
- Develop and maintain CI/CD pipelines for infrastructure and application deployments using Cloud Build, GitHub Actions, or equivalent tools.
- Implement infrastructure versioning, automated code reviews, governance controls, and reliable deployment strategies.
- Configure and manage GCP networking components, including VPCs, subnets, firewall rules, private connectivity, DNS, SSL certificates, and load balancing.
- Implement and maintain IAM policies, service accounts, secrets management, and security controls in accordance with enterprise security and compliance requirements.
- Build and support GCP-native data platforms using BigQuery, Dataflow, Pub/Sub, Cloud Storage, and Cloud Composer.
- Develop APIs, serverless functions, and data connectors using Python, Cloud Functions, and Cloud Run to support downstream applications and AI/ML systems.
- Implement monitoring, logging, alerting, and observability solutions using Cloud Monitoring and related GCP tools.
- Ensure infrastructure reliability, scalability, high availability, performance, and disaster recovery readiness across cloud environments.
- Troubleshoot production incidents, perform root cause analysis, and implement corrective and preventive measures.
- Develop and maintain operational runbooks, architecture documentation, deployment procedures, and production support guidelines.
- Collaborate with Data Engineering, application development, and AI/ML teams to deliver secure and scalable cloud platforms.
- Provision and maintain infrastructure required for AI-driven workloads, including Vertex AI Agent Engine, Vertex AI Search, and Model Armor.
- Enforce infrastructure security, compliance, governance, and operational best practices across GCP environments.
Required Qualifications- Hands-on experience designing, provisioning, and managing infrastructure on Google Cloud Platform (GCP).
- Strong proficiency in Terraform, including reusable modules, infrastructure state management, environment configuration, and Infrastructure as Code best practices.
- Experience building and maintaining CI/CD pipelines using Google Cloud Build, GitHub Actions, or equivalent automation tools.
- Practical experience with GCP services such as Cloud Run, BigQuery, Cloud Storage, Firestore, Secret Manager, and Cloud Functions.
- Strong understanding of GCP networking, including VPCs, subnets, firewall rules, private connectivity, DNS, and SSL certificates.
- Experience implementing IAM policies, service accounts, secrets management, and cloud security controls.
- Experience with containerized and cloud-native platforms, particularly GKE and Cloud Run.
- Proficiency in Python for automation, API development, data integration, or cloud engineering tasks.
- Familiarity with GCP data engineering services, including BigQuery, Dataflow, Pub/Sub, Cloud Storage, and Cloud Composer.
- Experience implementing cloud monitoring, logging, alerting, and production troubleshooting processes.
- Understanding of cloud architecture principles, high availability, scalability, disaster recovery, and infrastructure governance.
- Strong analytical, problem-solving, documentation, and cross-functional collaboration skills.
Preferred Qualifications- Experience supporting AI/ML infrastructure and workloads on Google Cloud.
- Familiarity with Vertex AI Agent Engine, Vertex AI Search, and Model Armor.
- Experience building APIs and data connectors for enterprise applications, data platforms, or AI/ML solutions.
- Experience implementing automated infrastructure validation, code reviews, security checks, and policy enforcement.
- Knowledge of enterprise cloud security, compliance requirements, and governance frameworks.
- Experience supporting production cloud environments and participating in incident management and root cause analysis.
Key Technical Skills
Cloud Platform: Google Cloud Platform (GCP)
Infrastructure as Code: Terraform, reusable modules, infrastructure versioning, IaC best practices
CI/CD & Automation: Cloud Build, GitHub Actions, deployment automation, automated code reviews
GCP Services: Cloud Run, Cloud Functions, BigQuery, Cloud Storage, Firestore, Secret Manager, Cloud Load Balancing
Data Platforms: Dataflow, Pub/Sub, Cloud Composer, BigQuery
Networking & Security: VPC, subnets, firewall rules, private connectivity, DNS, SSL/TLS, IAM, service accounts
Containers: GKE, Cloud Run
Programming & Integration: Python, REST APIs, data connectors
Monitoring & Operations: Cloud Monitoring, logging, alerting, observability, incident management, root cause analysis
AI/ML Infrastructure: Vertex AI Agent Engine, Vertex AI Search, Model Armor