Senior Forward Deployed Engineer, Gemini Enterprise Platform

AuxoAI is building the Forward-Deployed AI Company. The company works alongside Global 2000 enterprises to understand how their businesses operate, reimagine how work gets done with AI, and build production systems that deliver measurable business outcomes. AuxoAI has a unique approach of forward deploying teams of AI strategists and engineers, and accelerating deployment with Damia, its IP FDE workbench.

Headquartered in the San Francisco Bay Area, AuxoAI is an AI-native Google Gemini Enterprise transformation partner and an AWS advanced-tier partner. Learn more at .

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.

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

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 equivalent) with

infrastructure-as-code (Terraform).

7. Client-facing or embedded delivery experience — able to pair with a client's engineers and hand over cleanly.

• 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).

Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits.

Auxo.ai is an equal opportunity employer and is committed to providing equal employment opportunities to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, gender identity, or genetics.