Google Cloud Platform (GCP) Architect
L4 GCP AI Cloud Architect at Tredence defining enterprise-scale cloud and AI platform architecture on Google Cloud — designing GKE/Kubernetes platforms, Vertex AI/GenAI solutions (RAG, LLMs, AI agents), and setting standards for IaC (Terraform), MLOps, DevSecOps, and observability while mentoring engineering teams. Offshore role in Hyderabad, Pune, or Bengaluru.
- Compensation
- Not disclosed
- Location
- Hyderabad; Pune; Bengaluru
- Work setup
- Hybrid
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 8+ years
- Category
- Architecture
- Posted
Skills
Must have
- 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 cloud infrastructure and AI workloads
- Strong leadership, communication, and stakeholder management skills
Full 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).