Gen AI Solution Architect

Job Description

We are seeking a Generative AI Solution Architect to lead the design and architecture of end-to-end Generative AI and multi-agent systems. This role is responsible for building scalable, secure, and production-grade GenAI solutions leveraging components such as RAG pipelines, LLM Gateways, and agent architectures. The GenAI SA ensures best-practice implementation, including A2A interoperability and the use of Vertex AI Agent Builder / ADK on Google Cloud.

Key Responsibilities

• Architect end-to-end Generative AI solutions, including RAG pipelines, LLM Gateways, vector stores, and agent-based architectures.

• Design secure, scalable, and interoperable multi-agent systems following A2A (Agent-to-Agent) principles.

• Leverage Vertex AI, including Agent Builder, ADK, Model Garden, and orchestration frameworks.

• Define architecture patterns for prompt engineering, grounding, tool integrations, and LLM operations.

• Ensure compliance, performance, observability, and governance across all GenAI components.

• Collaborate across ML, data engineering, application engineering, and security teams to deliver enterprise-grade GenAI platforms.

• Establish best practices for evaluation, monitoring, safety, and responsible AI in agentic systems.

Required Skills

• Strong experience architecting Generative AI and RAG-based solutions.

• Deep understanding of LLMs, embeddings, vector databases, and retrieval optimization patterns.

• Hands-on experience with Google Cloud Platform s Vertex AI (Agent Builder, ADK, vector search, LLM ops).

• Knowledge of multi-agent frameworks, orchestration patterns, and tool/skill integrations.

• Expertise in Python and modern AI/ML frameworks (LangChain, LangGraph, LlamaIndex, etc.).

• Strong understanding of security, scalability, and compliance for AI systems.

Preferred

• Experience with enterprise AI governance, evaluation frameworks, and safety tooling.

• Google Cloud Platform or AI/ML architecture certifications.