GCP AI Cloud Architect

Role Description

Job Description – GCP AI Cloud Architect (L4)

Role Level: Cloud Architect – GCP & AI (L4)

Location: Offshore

Role Overview

The GCP AI Cloud Architect (L4) is responsible for defining and leading the architecture of enterprise-scale cloud and AI platforms on Google Cloud Platform (GCP). This role requires deep expertise in GCP services, Kubernetes (GKE), cloud-native architectures, and modern AI/GenAI technologies. The architect will drive design standards, scalability, security, reliability, and innovation across cloud and AI ecosystems, enabling engineering teams to build resilient, intelligent, and high-performing solutions leveraging Generative AI, Machine Learning, and Data platforms.

Key Responsibilities

• Define and own end-to-end cloud and AI architecture for distributed systems on GCP.
• Architect scalable, secure, and highly available solutions using core GCP services.
• Design and implement Kubernetes-based platforms using GKE for microservices and containerized workloads.
• Lead architecture for AI/ML and Generative AI applications utilizing Vertex AI and managed GCP AI services.
• Design and implement Retrieval-Augmented Generation (RAG) architectures, Vector Databases, AI Agents, and LLM-powered solutions.
• Lead architecture for CI/CD, infrastructure automation, MLOps, and DevSecOps practices.
• Drive infrastructure design using Infrastructure as Code (Terraform).
• Establish best practices for cloud networking, IAM, security, governance, and responsible AI implementation.
• Design microservices, API-first, event-driven systems, and AI-integrated applications.
• Define observability frameworks including logging, monitoring, tracing, model monitoring, and AI performance metrics.
• Collaborate with application, data engineering, analytics, and AI/ML teams to design cloud-native and AI-driven solutions.
• Architect data pipelines and data platforms to support AI/ML workloads and GenAI applications.
• Evaluate and integrate Large Language Models (LLMs), AI agents, prompt engineering frameworks, and AI orchestration platforms.
• Provide technical leadership, mentorship, and governance across engineering and AI development teams.
• Drive cost optimization, performance tuning, reliability improvements, and AI infrastructure efficiency.
• Support adoption of modern engineering practices such as SRE, GitOps, Automation, MLOps, and Agentic AI frameworks.
  
  

Required Skills And Experience

• 8–12 years of experience in cloud architecture, platform engineering, DevOps, or AI platform roles.
• Strong expertise in GCP architecture and core GCP services (Compute Engine, GKE, Cloud Storage, BigQuery, IAM, VPC).
• Hands-on experience with Kubernetes and Google Kubernetes Engine (GKE).
• Proven experience designing scalable, resilient, and secure cloud-native applications.
• Strong understanding of AI/ML platform architecture and Generative AI solution design.
• Experience building AI/GenAI applications using Vertex AI, foundation models, and LLM frameworks.
• Knowledge of Agentic AI architectures, AI orchestration frameworks, and autonomous workflow systems.
• Hands-on experience with RAG architectures, Vector Databases, embeddings, and semantic search.
• Expertise in Infrastructure as Code (Terraform).
• Strong understanding of microservices, API design, distributed systems, and event-driven architecture.
• Experience with MLOps, model deployment, model monitoring, and AI lifecycle management.
• Knowledge of CI/CD pipelines, GitOps, and DevSecOps practices.
• Deep understanding of cloud networking, RBAC, identity, secrets management, policies, and zero-trust principles.
• Experience with observability and monitoring tools for both cloud infrastructure and AI workloads.
• Strong leadership, communication, and stakeholder management skills.