Google Cloud AI Architect (Agentic AI Solutions)

Virtusa•New York, NY

14h•Remote

About The Position

Virtusa is seeking a Google Cloud AI Architect specializing in Agentic AI platforms to drive AI solution architecture, presales support, and enterprise AI adoption for clients leveraging Google Cloud’s Generative AI ecosystem. This role will work closely with sales teams, clients, and engineering teams to design and deliver agentic AI solutions, intelligent automation systems, and enterprise AI platforms on Google Cloud. The architect will lead customer workshops, solution design, proposal development, and technical orals, helping clients accelerate AI-driven transformation initiatives. The ideal candidate will have strong experience designing AI agents, LLM-powered applications, and scalable AI platforms, along with deep expertise in Google Cloud AI services and emerging agentic frameworks.

Requirements

15+ years of experience in software engineering, AI/ML engineering, or enterprise architecture.
6+ years of experience working with Google Cloud Platform.
Proven experience designing AI/ML or Generative AI solutions on cloud platforms.
Experience supporting presales engagements, proposals, and enterprise solution architecture.
Strong experience with Google AI and cloud technologies including Gemini Enterprise, Agentspace, Agent Development Kit (ADK), Agent-to-Agent (A2A), Model Context Protocol (MCP), Vertex AI, Vertex AI Agent Engine, GKE, Cloud Run, Compute Engine, and Google Cloud GPU infrastructure.
Strong familiarity with open-source AI frameworks including LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, and other agent orchestration frameworks.
At least one Google Cloud certification (two preferred): Google Cloud Professional Machine Learning Engineer, Google Cloud Professional Cloud Architect, Google Cloud Professional Data Engineer.

Responsibilities

Partner with sales teams and client stakeholders to understand business problems and translate them into AI-driven solutions on Google Cloud.
Lead customer workshops, AI discovery sessions, and solution design engagements.
Develop end-to-end architectures for enterprise agentic AI solutions.
Support RFP/RFI responses, proposal writing, solution documentation, and technical presentations.
Participate in solution orals and executive briefings to articulate business value of AI solutions.
Contribute to solution estimation, deal shaping, and closing strategic engagements.
Design enterprise-grade agentic AI architectures on Google Cloud.
Build architectures leveraging Google AI technologies including Gemini Enterprise, Agentspace, Agent Development Kit (ADK), Agent-to-Agent (A2A) communication, Model Context Protocol (MCP), Vertex AI, and Vertex AI Agent Engine.
Architect multi-agent systems, autonomous workflows, and AI-powered enterprise applications.
Integrate LLMs with enterprise systems, APIs, knowledge bases, and business workflows.
Design and deploy scalable AI platforms and LLM-powered applications on Google Cloud.
Architect AI workloads using Google Cloud infrastructure services including Vertex AI, GKE, Cloud Run, Compute Engine, and AI infrastructure with GPUs and accelerators.
Define deployment strategies for AI agents using GKE, Cloud Run, Vertex AI Agent Engine, and serverless architectures.
Provide guidance on model lifecycle management, model deployment, and scalable inference architectures.
Implement LLMOps and AI platform engineering practices for scalable AI solutions.
Ensure monitoring, governance, observability, and reliability of AI systems.
Architect RAG pipelines, vector databases, and knowledge-driven AI systems.
Evaluate and integrate open-source AI and agentic frameworks to enhance enterprise solutions.
Work with frameworks and tools such as LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, and other agent orchestration platforms.
Combine Google Cloud AI services with open-source AI ecosystems to build flexible architectures.
Design cost-efficient AI architectures aligned with FinOps principles.
Optimize GPU usage, inference workloads, and AI infrastructure costs.
Implement scalable AI architectures that can support enterprise workloads and future growth.
Develop innovative AI accelerators, reusable agent frameworks, and solution blueprints.
Track emerging trends in agentic AI, LLM platforms, and AI infrastructure.
Provide thought leadership to clients on enterprise AI adoption and next-generation AI architectures.
Provide architecture guidance during project execution.
Ensure solutions align with security, scalability, governance, and operational best practices.