This is remote role for India applicants. SCOPE OF WORK Build MCP servers, either leveraging FastMCP built deployed to Cloudrun, or using a service like Workato to build them. Own the full consumption chain: how an ADK agent (and/or Gemini Enterprise directly) discovers, authenticates to, and calls each server — JSON-RPC over Streamable HTTP, tool discovery, and the session handshake. Implement per-user, permission-aware authentication so every call runs as the asking user and the upstream system enforces that user's own permissions — OAuth 2.0 authorization-code flow with PKCE, refresh tokens / offline access, and secure token forwarding.
Build and deploy agents on Vertex AI Agent Engine using ADK, wire their tools and OAuth authorizations, and register/publish them into Gemini Enterprise for testing MCP. Operate what you ship: monitor and debug MCP servers and agents in production across the whole chain (auth handshake, tool discovery, agent runtime). Follow secure-by-default and governance practices: least privilege, no secrets in code, private/network-restricted services, cataloging and access control.
REQUIRED SKILLS (MUST HAVE) End-to-end MCP fluency — not only building a server, but how agents consume it: the protocol (JSON-RPC / Streamable HTTP), tool discovery, and the session handshake. Hands-on OAuth 2.0 / OIDC for agent scenarios — authorization-code flow, PKCE, refresh tokens, scopes, and per-user token forwarding so calls run as the signed-in user. Hands-on Google Vertex AI — Agent Engine, ADK, Gemini models — plus Cloud Run, IAM / service accounts, and Secret Manager.
Proven production-grade delivery (reliability, maintainability, scalability) — not just prototypes. Enterprise security discipline — least privilege, no secrets in code, private services, no token leakage, fail-closed design. Strong Python (or comparable) for AI and cloud development. 5+ years senior software development, with 2+ years building AI/agent solutions in an enterprise environment.
NICE TO HAVE
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