Senior Forward Deployed Engineer, Gemini Enterprise Platform

Role Summary

You are the engineer who makes the outcome real. As a Senior Forward Deployed Engineer, you take a client's use

case from a whiteboard to a governed, evaluated agent that people genuinely use — and you measure your work by

the value it creates, not the code you shipped. Embedded with the client, you build the agents, the tools they call

and the context graph they reason over on the Gemini Enterprise Agent Platform: composing them in ADK or on an

Agent Garden template, grounding them on a BigQuery or Spanner Graph foundation, wiring them to data and

systems through MCP, deploying on Agent Engine / Cloud Run / GKE, and publishing them into the client's Gemini
Enterprise catalog.

You are close enough to the client's engineers to pair with them, and close enough to the platform to debug a failing

agent trajectory — and disciplined enough to leave behind something the client can own, trust and extend.

This role exists because the value of a Gemini Enterprise program is realised one working, adopted agent at a time

- and that takes an engineer who can build to a production bar and operate credibly inside a client's environment.

Deployment Model

Embedded in a client engagement, usually alongside a Principal Forward Deployed Architect who owns the overall

design. You pair with the client's own engineers and are expected to leave them able to maintain and extend what

you built. Some pre-sales support is expected — proofs of concept, demos and effort inputs.
Key Responsibilities

Agent build

- Build agents ground-up in ADK and by forking and hardening Agent Garden templates — defining instructions,

model selection (Model Garden), tools, orchestration (LLM-driven and deterministic workflow agents),
grounding and memory.

- Select and bind models per agent or per step for cost and latency; implement structured output, thinking-level

and safety configuration.

- Run evaluation and simulation before ship — trajectory and response metrics, synthetic-user simulation — and

act on Agent Optimizer findings.
Tools, MCP and integration

- Build MCP servers to expose client systems and data as agent tools; integrate off-the-shelf and third-party MCP

servers; wire OpenAPI and Google Cloud toolsets.

- Implement multi-agent (A2A) hand-offs where the design calls for them.

Context graph and data

- Build the context-graph foundation on BigQuery graph (GQL) and/or Spanner Graph, and the retrieval /

grounding path (Vertex AI, Vector Search, Embeddings, RAG) that connects it to agents.

- Build and operate the supporting data stack: BigQuery models, Dataform pipelines, Dataproc jobs and Pub/Sub

streams, with cataloguing, lineage and classification in Dataplex Universal Catalog / Knowledge Catalog.
Deploy, operate and adopt

- Deploy agents to Agent Engine, Cloud Run or GKE via the Agents CLI and infrastructure-as-code; instrument

observability (Cloud Trace / OpenTelemetry); apply governance (Model Armor, Semantic Governance, Agent

Identity).•Publish agents into the client's Gemini Enterprise app catalog and configure Google Workspace int

Requirements

Minimum Qualifications

1.Master's or Bachelor's degree in Computer Science, Engineering or a related field, or equivalent practical
experience.

2.6+ years building and shipping production software or data / ML systems, with strong Python.

3.Hands-on experience building LLM agents with a code-first framework (ADK preferred; LangGraph, CrewAI,

LlamaIndex or Amazon Bedrock Agents accepted) — including tools, retrieval grounding and evaluation.

4.Strong BigQuery and SQL, and hands-on experience with at least one graph store (Spanner Graph, BigQuery
graph, Neo4j or equivalent).

5.Built at least one data pipeline in production (Dataform, Dataproc / Spark, dbt or equivalent) and worked with a

streaming / eventing system (Pub/Sub or equivalent).

6.Deployed services to a managed or container runtime (Cloud Run, GKE, Kubernetes or