Senior Forward Deployed Engineer, Gemini Enterprise Platform
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Job Description
Senior Forward Deployed Engineer building production AI agents, data pipelines, and context graphs for Google's Gemini Enterprise Platform. Embedding with clients to deploy, govern, and transfer maintainable solutions.
Responsibilities:
- 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 using trajectory and response metrics and synthetic-user simulation; act on Agent Optimizer findings
- Build MCP servers to expose client systems and data as agent tools; integrate third-party MCP servers; wire OpenAPI and Google Cloud toolsets
- Implement multi-agent (A2A) hand-offs where required
- Build context-graph foundations on BigQuery graph and/or Spanner Graph, plus retrieval and grounding paths using Vertex AI, Vector Search, Embeddings and RAG
- Build and operate supporting data stacks including BigQuery models, Dataform pipelines, Dataproc jobs and Pub/Sub streams, with cataloguing, lineage and classification in Dataplex Universal Catalog / Knowledge Catalog
- Deploy agents to Agent Engine, Cloud Run or GKE via the Agents CLI and infrastructure-as-code
- Instrument observability with Cloud Trace / OpenTelemetry and apply governance using Model Armor, Semantic Governance and Agent Identity
- Publish agents into the client's Gemini Enterprise app catalog and configure Google Workspace integration
- Pair with client engineers and leave them able to maintain and extend the delivered solution
- Provide pre-sales support through proofs of concept, demos and effort inputs
Requirements:
- 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
- Google Cloud Professional certification (Data Engineer, Machine Learning Engineer, or Cloud Developer) (preferred)
- Hands-on with the Gemini Enterprise Agent Platform — ADK, Agent Garden, Model Garden, Agent Engine, Agent Studio, Agents CLI (preferred)
- Built or operated MCP servers, and integrated third-party MCP servers into an agent (preferred)
- Built a retrieval / grounding layer over a knowledge or context graph (preferred)
- Experience with Gemini Enterprise app publishing and Google Workspace integration (preferred)
- Experience with agent evaluation and observability at production scale (autoraters, trajectory metrics, Cloud Trace) (preferred)
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