Description

Key Responsibilities

• Own the architecture of the enterprise Gemini platform including managed chat, enterprise search, agent gallery, and custom front-end applications
• Design and govern the Agent Platform layer — ADK and agent runtime, agent and tool registry, agent identity, security, and observability
• Architect integration and data layers across Google Workspace, Microsoft 365, MCP servers, and federated retrieval across function-scoped data stores
• Manage identity and access patterns including OAuth2, SAML, RBAC, token exchange, and document-level ACL mapping into retrieval layers
• Drive cost awareness across token and inference spend as the agent estate grows
• Establish AI governance functions including review boards, architecture checklists, and audit evidence

Required Skills

• Google Gemini Enterprise and ADK (or comparable enterprise agent platform)
• Multi-agent systems in production — orchestration, routing, tool use, memory, human-in-the-loop
• RAG architecture: vector stores, embedding models, chunking, hybrid search
• Identity and access: OAuth2, SAML, RBAC, token exchange, service-account and end-user credential propagation
• Python (proficient); Go or equivalent second language (comfortable)
• MCP — building servers, not only consuming them
• Google Cloud Platform: Cloud Run, GKE, Vertex AI, networking, IAM

Preferred Skills

• Production deployment of agents on Gemini Enterprise / Agent Platform
• AI evaluation tooling: Langfuse, LangSmith, Braintrust, or custom eval frameworks
• Multi-model routing across Gemini, Claude, OpenAI, or Llama
• Enterprise data connector work across Google Workspace and Microsoft 365
• AI governance experience inside regulated or multi-entity enterprises
• Containerisation and orchestration: Docker, Kubernetes

Key Skills

Education

Any Graduate