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.