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.

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.

Key Responsibilities:

Agent build:

- Build agents ground-up in ADK and by forking and hardening Agent Garden templates - defining instructions, model selection, tools, orchestration, 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.

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.

- 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.

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).

- 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.