AI Architect - Hybrid 3 Days in office-San Jose , CA

We are seeking a highly skilled, hands-on Lead AI Systems Architect to design, build, and scale our next-generation AI ecosystem. In this role, you will lead the architecture and implementation of Agent-to-Agent frameworks, Model Context Protocol (MCP) integrations, and our proprietary AI development platform.

This is not a purely theoretical role. You must be deeply technical, actively writing code, and capable of translating complex AI research into robust, production-grade software. As a key bridge between engineering teams and stakeholders, you must also possess exceptional communication skills to articulate technical vision and drive cross-functional alignment.

Core Responsibilities:

• Agent-to-Agent Architecture: Design and implement multi-agent orchestration frameworks, focusing on autonomous negotiation, task delegation, state management, and secure inter-agent communication.
• MCP Integration: Architect and build scalable Model Context Protocol (MCP) servers and clients to securely connect LLMs to data sources, development tools, and enterprise environments.
• AI Platform Development: Drive the architectural vision for our internal AI development platform, ensuring high throughput, low latency, and seamless developer workflows for training, testing, and deploying models.
• Hands-on Engineering: Write high-quality, production-ready code, build prototypes, and establish engineering best practices for AI application development.
• Technical Leadership & Communication: Standardize architectural patterns across the organization. Mentor junior engineers and explain complex technical concepts to non-technical stakeholders clearly and effectively.

• Experience: 10+ years of software architecture experience, with at least 3+ years of dedicated, hands-on experience building and deploying production-grade LLM applications and AI systems.
• AI & Agentic Expertise: Deep understanding of multi-agent frameworks (e.g., LangGraph, AutoGen, CrewAI), prompt engineering, RAG pipelines, and vector databases.
• Protocol & API Mastery: Proven experience with Model Context Protocol (MCP) or designing similar context-sharing and tool-calling protocols for GenAI.
• Technical Stack: Proficiency in Python, TypeScript, or Go, along with cloud native technologies (AWS/Google Cloud Platform/Azure, Kubernetes, Docker) and modern AI SDKs.
• Communication: Outstanding verbal and written communication skills, with a proven ability to lead technical discussions and author comprehensive architecture RFCs.