Gemini AI Architect

Role: Gemini AI Architect

Location : San Ramon, CA

Skills : Google Gemini Enterprise · Google ADK · Agent Registry · MCP · RAG · AI Governance · Identity & Entitlements · Observability & FinOps · Pytho

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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 estat

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aveProduction 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 quick

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