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 

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

1. 6+ years building and shipping production software or data / ML systems, with strong Python. 
   

1. 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. 
   

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

1. 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). 
   

1. Deployed services to a managed or container runtime (Cloud Run, GKE, Kubernetes or equivalent) with infrastructure-as-code (Terraform). 
   

1. 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).