Google Cloud Platform: Compute Engine, GKE, Cloud Storage, BigQuery, Cloud Build, IAM,
VPC, Cloud Monitoring and Cloud Logging
• AI/ML: Vertex AI, plus agentic systems and emerging protocols such as MCP and Agent2Agent
(A2A)
• Infrastructure as Code & Automation: Terraform (Google provider), Ansible, Git, CI/CD
pipelines
• Security & Governance: Organization Policies, VPC Service Controls, Security Command
Center, DevSecOps tooling
• Operating Systems & Scripting: Linux (RHEL), Python
• Networking: Shared VPC, Cloud Interconnect/VPN, Private Service Connect
Roles and Responsibilities
• Architect & Optimize: Design and implement Google Cloud Platform environments for reliability, scalability, and
security, focusing on AI/ML workflows on Vertex AI and enterprise-grade platforms.
• Cloud Migration: Lead migrations of applications and workloads from on-premises data centers
to Google Cloud Platform, covering assessment, planning, cutover and post-migration optimization.
• Reliability & Operations: Own the day-to-day reliability of production Google Cloud Platform platforms, including
monitoring, alerting, incident response, capacity planning and cost optimization.
• Automation & CI/CD: Build and maintain Terraform-based infrastructure pipelines and zero
touch automation so environments are provisioned consistently and securely.
• Platform Enablement: Develop and enforce reusable Google Cloud Platform patterns (landing zones, Terraform
modules, project and folder structures), automation frameworks, and best practices for cloud and
AI/ML deployments.
• Security & Governance: Establish and manage Google Cloud Platform security controls (IAM, Organization
Policies, VPC Service Controls, Security Command Center), governance processes, and
compliance standards.
• Collaboration: Work closely with business and technology teams to align Google Cloud Platform platform
capabilities with organizational goals.
• Client Engagement: Serve as the go-to technical voice for cloud infrastructure on the project
team, and present designs and recommendations to CGI and client stakeholders.
• Technical Leadership: Mentor and support engineering teams, guide data scientists and ML
engineers on Google Cloud Platform and Vertex AI best practices, and lead resolution of complex platform issues.
• Continuous Improvement: Stay current with Google Cloud releases and industry trends,
evaluate emerging technologies, and drive adoption of cloud engineering best practices.
Required Qualifications
• Deep, hands-on expertise in Google Cloud Platform (Google Cloud Platform), including Compute Engine, GKE,
Cloud Storage, BigQuery, Cloud Build, IAM, and VPC.
• 8+ years of relevant experience in cloud engineering, with a strong background in architecting
and operating Google Cloud Platform environments at scale.
• Advanced proficiency in Terraform for Google Cloud Platform infrastructure automation; familiarity with Ansible and
Git.
• Strong understanding of AI platforms and AI/ML workflows, with hands-on Vertex AI experience.
• Familiarity with agentic systems and emerging AI protocols such as Model Context Protocol
(MCP) and Agent2Agent (A2A).
• Advanced knowledge of Linux (RHEL) and Python for automation and tooling.
• Demonstrated experience with DevSecOps, zero-touch automation, and shift-left security
practices.
• Advanced understanding of Google Cloud Platform networking (Shared VPC, Cloud Interconnect/VPN, Private
Service Connect), security principles, and compliance frameworks.
• Experience with Behavior Driven Development (BDD) for testing.
• Excellent problem-solving, analytical, and communication skills.
• Ability to work effectively in a collaborative, cross-functional team environment.
• Adaptability to changing technologies and project requirements.
Nice to Have
• Google Cloud Professional certification(s): Cloud Architect, Cloud DevOps Engineer, Cloud
Security Engineer, or Machine Learning Engineer.
• Experience with other cloud providers (AWS, Azure) and their AI platforms (SageMaker,
Bedrock, Copilot).
