[Hiring] Google Cloud AI Architect @Virtusa

Role Description

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.

Qualifications

• 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.

Requirements

• 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.

Benefits

• Design enterprise-grade agentic AI architectures on Google Cloud.
• Build architectures leveraging Google AI technologies.
• 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.
• Implement LLMOps and AI platform engineering practices for scalable AI solutions.
• Evaluate and integrate open-source AI and agentic frameworks to enhance enterprise solutions.
• Design cost-efficient AI architectures aligned with FinOps principles.
• Develop innovative AI accelerators, reusable agent frameworks, and solution blueprints.
• Provide architecture guidance during project execution.

Certifications

• At least one Google Cloud certification (two preferred):
• Google Cloud Professional Machine Learning Engineer
• Google Cloud Professional Cloud Architect
• Google Cloud Professional Data Engineer