GCP AI Cloud Architect

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.