Gemini AI Architect

Job Title: Gemini AI Architect

Location: San Ramon, CA (Hybrid)

Hire Type: Contract

Gemini AI Architect:

Keywords: Google Gemini Enterprise Google ADK Agent Registry MCP RAG AI Governance Identity & Entitlements Observability & FinOps Python

The Platform Environment

• Gemini Enterprise - the employee-facing surface: managed chat, enterprise search, agent gallery, and app-scoped experiences serving all business functions.
• Agent Platform - the build, govern and operate layer: ADK and agent runtime, agent and tool registry, agent identity, agent security, agent observability.
• Custom front-end applications calling Gemini Enterprise via API, with end-user identity propagation.
• Integration and data layer - enterprise connectors across Google Workspace and Microsoft 365, BYO and vendor-managed MCP servers, and federated retrieval across function-scoped data stores.

What We're Looking For

• 10+ years in software engineering and architecture, with 3+ years designing and running applied AI systems in production.
• Proven experience as the architectural owner of an enterprise platform - you have set standards that other teams had to follow, and made them stick.
• Hands-on with Google Gemini Enterprise and ADK , or a directly comparable enterprise agent platform, including agent runtime, registration, identity and observability.
• Deep experience with multi-agent systems in production - orchestration, routing, tool use, memory, human-in-the-loop - with real operational ownership rather than prototype work.
• Strong grounding in RAG and retrieval architecture : vector stores, embedding models, chunking strategy, hybrid search, and the difference between retrieval that works in a demo and retrieval that works across a messy enterprise estate.
• Identity and access depth. OAuth2, SAML, RBAC, token exchange, service-account versus end-user credential propagation, and document-level ACL mapping from source systems into a retrieval layer. You understand why this determines whether governance is real.
• Proficient in Python ; comfortable with Go or an equivalent second language.
• Experience with MCP - building servers, not only consuming them.
• Solid cloud-native and systems fundamentals, with GCP strongly preferred (Cloud Run, GKE, Vertex AI, networking, IAM).
• Cost awareness at scale - token and inference spend management across a growing agent estate.

Nice to Have

• Production deployment of agents on Gemini Enterprise / Agent Platform, including custom agents and search experiences.
• Experience with AI evaluation tooling - agent observability platforms, Langfuse, LangSmith, Braintrust, or custom eval frameworks.
• Multi-model routing and fallback across Gemini, Claude, OpenAI or Llama, balancing capability, latency and cost.
• Enterprise data connector work across Google Workspace and Microsoft 365, including entitlement-aware indexing.
• Experience standing up an AI governance function - review boards, architecture checklists, audit evidence - inside a regulated or multi-entity enterprise.
• Containerisation and orchestration (Docker, Kubernetes).
• Fluent use of AI-assisted development tooling to move quickly.