Principal Forward Deployed Architect, GCP

Role Summary You are the person a client trusts to turn an ambitious Gemini Enterprise vision into a business outcome that lasts. As Principal Forward Deployed Architect on an account, you own the result -- the value the client set out to create — and with it the technical whole that produces that value: the GenAI platform foundation on Google Cloud, the agent landscape built on the Gemini Enterprise Agent Platform (GEAP), the context-graph and data foundation those agents reason over, and the enterprise rollout into the Gemini Enterprise app. Where specialist engineers each own an individual agent, MCP server or data pipeline, you own the whole — deep in the agent platform and the context / data foundation, fluent enough across governance, runtime and adoption to design, sequence and defend the program end to end. You are the senior technical counterpart the client's executives call before they have decided what to build; more importantly, you are the reason they keep calling. You make Google Cloud's AI foundation deliver the outcomes. This role exists because standing up a production agent ecosystem on GEAP is not a single-layer problem — model choice, agents, grounding graph, governance perimeter and change management are load-bearing on one another — and because our largest clients will accept only one senior technical owner rather than several. Deployment Model Placed at one large Gemini Enterprise account, or holding technical ownership across two or three smaller concurrent engagements. You may direct AuxoAI delivery teams, including offshore and onshore Forward Deployed Engineers and client engineers, on the same program — you own the design coherence across it. Significant pre-sales involvement is expected: the GEAP target architecture, the GCP landing-zone approach, effort estimates, and the technical case in proposals for the practice's largest Gemini opportunities. Key Responsibilities Whole-program architecture Own the target architecture across four layers — GCP GenAI platform foundation, the GEAP agent landscape, the context-graph / data foundation, and enterprise adoption — and sequence delivery across all four. Set the reference patterns for how agents are built (ground-up in ADK vs. forked and hardened from Agent Garden templates), where they run (Agent Engine managed vs. Cloud Run vs. self-managed GKE), how they are isolated (sandbox strategy), and how they are governed. Design the context-graph foundation — BigQuery, BigQuery graph (GQL) and/or Spanner Graph — and the grounding / RAG strategy that connects it to agents, including entity resolution, semantic modelling and retrieval over Vertex AI Vector Search. Identify decisions in one layer that are load-bearing for others (e.g., a grounding-data residency choice that constrains the runtime target and the governance perimeter) and force them to resolution before delivery commits, not during it. Arbitrate cross-track trade-offs where multiple Forward Deployed Engineers are…