Google Cloud Platform Cloud Architect (L5)

HMG America LLC is the best Business Solutions focused Information Technology Company with IT consulting and services, software and web development, staff augmentation and other professional services. One of our direct clients is looking for Google Cloud Platform Cloud Architect (L5) in Remote. Below is the detailed job description.

Title: Google Cloud Platform Cloud Architect (L5)

Location: Remote

Job Description:
Role Overview

We are looking for an experienced L5 Senior Google Cloud Platform Cloud and AI Architect to lead the design and implementation of secure, scalable, and enterprise-grade cloud and AI platforms on Google Cloud Platform.
The role requires strong architectural depth across Google Cloud Platform infrastructure, networking, IAM, DevOps, platform engineering, and AI/ML services, with a specific focus on enabling Vertex AI-based GenAI and Agentic AI workloads.
The ideal candidate should have hands-on experience in architecting cloud-native platforms, defining landing zone patterns, implementing Infrastructure as Code using Terraform, and enabling modern AI solutions using Vertex AI services, Gemini models, embeddings, RAG, and AI agent frameworks.
This is a senior architect-level role that requires the ability to work closely with client architects, engineering pods, platform teams, SREs, AI engineers, and global stakeholders while aligning to CDT business hours. Key Responsibilities

1. Google Cloud Platform Cloud Architecture and Platform Engineering

• Lead the architecture, design, and implementation of scalable and secure Google Cloud Platform cloud platforms.
• Define enterprise-grade Google Cloud Platform landing zones, multi-project structures, shared services, and governance models.
• Architect workloads across Google Cloud Platform services including GKE, Vertex AI, Cloud Run, Cloud Storage, Pub/Sub, BigQuery, and Cloud Functions.
• Design cloud-native patterns for compute, storage, networking, data, security, and reliability.
• Provide architecture guidance for modernization, migration, and cloud-native application deployments.
• Establish best practices for platform engineering and internal developer platforms.

2. Infrastructure as Code and DevOps Architecture

• Design and implement reusable, scalable, and governed Infrastructure as Code modules using Terraform.
• Define Terraform standards for multi-environment deployments, state management, module structure, and policy enforcement.
• Architect CI/CD pipelines using GitHub Actions, or equivalent tools.
• Enable automated provisioning, deployment, monitoring, and release governance across Google Cloud Platform environments.
• Provide technical leadership for DevOps, GitOps, and platform automation practices.
• Ensure deployment pipelines are secure, auditable, and aligned with enterprise compliance standards.

3. Google Cloud Platform Networking and Connectivity Architecture

• Architect advanced Google Cloud Platform networking solutions including VPCs, Shared VPCs, routing, firewall policies, DNS, NAT, and load balancing.
• Design hybrid and multi-cloud connectivity using Cloud VPN, Dedicated/Partner Interconnect, and Private Service Connect.

4. IAM, Security, and Governance

• Define and implement enterprise IAM models using least privilege, RBAC, service accounts, workload identity, and policy controls.
• Design secure access patterns for applications, DevOps pipelines, AI workloads, and platform services.
• Ensure cloud environments comply with client security, compliance, and operational standards.
• Provide guidance on secrets management, encryption, identity federation, and secure service-to-service communication.

5. Vertex AI, GenAI, and Agentic AI Architecture

• Architect AI/ML and GenAI solutions using Vertex AI services, including Gemini models, Model Garden, Vertex AI Pipelines, Vertex AI Vector Search, and managed ML workflows.
• Design enterprise-grade GenAI patterns such as RAG, embeddings, prompt orchestration, model evaluation, grounding, and responsible AI controls.
• Collaborate with AI engineers and data teams to operationalize GenAI, MLOps, and intelligent automation use cases.

6. API, Streaming, and Integration Architecture

• Design API-led and event-driven integration patterns for AI, microservices, and enterprise platforms.
• Architect streaming and event-driven workloads using Pub/Sub and platforms such as Confluent Kafka.
• Define secure, scalable, and observable integration patterns across cloud and enterprise systems.

7. Architecture Leadership and Delivery Collaboration

• Lead architecture discussions with client stakeholders, engineering teams, SREs, AI engineers, and platform teams.
• Translate business and technical requirements into actionable architecture blueprints and implementation roadmaps.
• Provide technical direction to engineering pods and review implementation quality.
• Create architecture artifacts, solution designs, reference patterns, and best-practice documentation.
• Support distributed delivery teams while aligning to CDT business hours for client collaboration.
• Drive technical decision-making across cloud, DevOps, networking, security, and AI platform areas.

Required Skills and Experience

• Strong hands-on experience with Google Cloud Platform architecture and implementation.
• Mandatory expertise in Terraform for Infrastructure as Code.
• Strong knowledge of Vertex AI services, including Gemini models and enterprise GenAI capabilities.
• Ability to design and guide implementation of AI/ML and GenAI solutions on Google Cloud Platform.
• Experience designing secure and scalable Google Cloud Platform landing zones and multi-project architectures.
• Deep understanding of Google Cloud Platform IAM, security, governance, and policy enforcement.
• Strong experience with Google Cloud Platform networking, including VPC, Shared VPC, routing, firewall rules, VPN, Interconnect, Private Service Connect, and NCC.
• Experience with cloud-native services such as GKE, Cloud Run, Cloud Storage, Pub/Sub, and BigQuery.
• Strong DevOps knowledge, including CI/CD pipelines, container platforms, automation, and observability.
• Experience with Kubernetes, Docker, and GKE architecture.

Preferred Qualifications

• Google Cloud Professional Cloud Architect certification.
• Experience with GenAI application architecture on Google Cloud Platform.
• Hands-on experience with Vertex AI and AI/ML solution design.
• Familiarity with data engineering and analytics workloads on Google Cloud Platform.
• Experience with GitOps tools and SRE practices.

Education

Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or a related field.