Senior Forward Deployed Engineer, Gemini Enterprise Platform
A senior engineer embedded in client engagements who takes AI agent use cases from whiteboard to production on Google's Gemini Enterprise Agent Platform — building agents in ADK/Agent Garden, grounding them on BigQuery/Spanner context graphs, wiring tools via MCP, and deploying on Cloud Run/GKE with Terraform while pairing with client engineers.
- Compensation
- Not disclosed
- Location
- Remote, United States
- Work setup
- Remote
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 6+ years
- Category
- Solutions engineering
- Posted
Skills
Must have
- Master's or Bachelor's degree in Computer Science, Engineering or 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 evaluatio
- 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 production data pipeline (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 client engineers and hand over cleanly
Nice to have
- 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)
Full description
About AuxoAI
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 .The Role
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.
What you will doAgent 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