Senior Forward Deployed Engineer, Gemini Enterprise Platform

This is a fully remote position, open to applicants in United States.

📋 Description

• Develop agents from the ground up using ADK and by modifying and enhancing Agent Garden templates, establishing guidelines, model selection, tools, orchestration, grounding, and memory.

• Choose and bind models for each agent or step based on cost and latency; implement structured output, thinking-level, and safety configurations.

• Conduct evaluations and simulations prior to release, utilizing trajectory and response metrics along with synthetic-user simulations; act based on findings from the Agent Optimizer.

• Construct MCP servers to present client systems and data as agent tools; integrate third-party MCP servers; connect with OpenAPI and Google Cloud toolsets.

• Implement multi-agent (A2A) transitions as necessary.

• Establish context-graph foundations on BigQuery graph and/or Spanner Graph, along with retrieval and grounding paths using Vertex AI, Vector Search, Embeddings, and RAG.

• Create and manage supporting data stacks, including BigQuery models, Dataform pipelines, Dataproc jobs, and Pub/Sub streams, with cataloging, 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.

• Enhance observability using Cloud Trace / OpenTelemetry and enforce governance with Model Armor, Semantic Governance, and Agent Identity.

• Publish agents into the client's Gemini Enterprise app catalog and configure Google Workspace integration.

• Collaborate with client engineers, empowering them to maintain and extend the delivered solution.

• Provide pre-sales support through proofs of concept, demonstrations, and effort assessments.

⛳️ Requirements

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

• Over 6 years of experience in building and deploying production software or data/ML systems, with a strong proficiency in Python.

• Practical experience in building LLM agents using a code-first framework (ADK preferred; LangGraph, CrewAI, LlamaIndex, or Amazon Bedrock Agents accepted), including tools, retrieval grounding, and evaluation.

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

• Developed at least one production data pipeline (Dataform, Dataproc/Spark, dbt, or equivalent) and engaged with a streaming/eventing system (Pub/Sub or equivalent).

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

• Experience in client-facing or embedded delivery; capable of collaborating with client engineers and ensuring a smooth handover.

• Google Cloud Professional certification (Data Engineer, Machine Learning Engineer, or Cloud Developer) is preferred.

• Hands-on experience with the Gemini Enterprise Agent Platform — ADK, Agent Garden, Model Garden, Agent Engine, Agent Studio, Agents CLI is preferred.

• Experience building or operating MCP servers and integrating third-party MCP servers into an agent is preferred.

• Developed a retrieval/grounding layer over a knowledge or context graph is preferred.

• Familiarity with Gemini Enterprise app publishing and Google Workspace integration is preferred.

• Experience in agent evaluation and observability at a production scale (autoraters, trajectory metrics, Cloud Trace) is preferred.

🏝️ Benefits

• Competitive salary and performance-based bonuses.

• Comprehensive health, dental, and vision insurance.

• Flexible working hours and remote work options.

• Opportunities for professional development and continuous learning.

• Collaborative and innovative work environment.