Senior Forward Deployed Engineer, Gemini Enterprise Platform (GCP)

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

• integration.

• Support adoption: onboarding

• materials, runbooks, and pairing with client users and engineers.

• Outcome

• Ownership

• You own the

• outcome of what you build — through production, handover and adoption.

• Grounded, evaluated, governed, deployed, documented, and actually used. When

• one of your agents fails, regresses or breaches a policy in production, you own

• the fix and the honest post-incident note.

• Technical

• Environment

• Area

• Technologies

• Agent build (GEAP)

• ADK (Python), Agent Garden templates, Agent Studio,

• Agents CLI, agent types & orchestration, tools (FunctionTool,

• OpenAPIToolset, McpToolset), Model Garden model selection

• MCP & integration

• MCP server development, off-the-shelf and

• third-party MCP servers, A2A, OpenAPI, Google Cloud connectors / toolsets

• Context graph & retrieval

• BigQuery graph (GQL), Spanner Graph, Vertex AI

• Vector Search, embeddings, RAG / grounding pipelines, entity resolution

• Data engineering

• BigQuery (SQL), Dataform, Dataproc (Spark), Pub/Sub,

• Python, Dataplex Universal Catalog / Knowledge Catalog

• Runtime & deployment

• Agent Engine, Cloud Run, GKE, Terraform, Cloud

• Build, Artifact Registry, Cloud Trace / OpenTelemetry, IAM

• Quality & governance

• Agent Evaluation (trajectory + autoraters), Agent

• Simulation, Agent Optimizer, Model Armor, Semantic Governance

• Minimum

Qualifications

• Master's or Bachelor's degree

• in Computer Science, Engineering or a related field, or equivalent

• practical experience.

• 6+ years building and shipping

• production software or data / ML systems, with strong Python.

• 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.

• Strong BigQuery and SQL, and

• hands-on experience with at least one graph store (Spanner Graph, BigQuery

• graph, Neo4j or equivalent).

• 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).

• Deployed services to a managed

• or container runtime (Cloud Run, GKE, Kubernetes or equivalent) with

• infrastructure-as-code (Terraform).

• Client-facing or embedded

• delivery experience — able to pair with a client's engineers and hand over

• cleanly.

• Preferred

Qualifications

• Hands-on with the Gemini

• Enterprise Agent Platform — ADK, Agent Garden, Model Garden, Agent Engine,

• Agent Studio, Agents CLI.

• Built or operated MCP servers,

• and integrated third-party MCP servers into an agent.

• Built a retrieval / grounding

• layer over a knowledge or context graph.

• Experience with Gemini

• Enterprise app publishing and Google Workspace integration.

• Experience with agent

• evaluation and observability at production scale (autoraters, trajectory

• metrics, Cloud Trace).

• Google Cloud Professional

• certification (Data Engineer, Machine Learning Engineer, or Cloud

• Developer).