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engineering opportunity

Forward Deployed Engineer IV, GenAI, Google Cloud

The engineer will architect and build production-grade agentic AI solutions by integrating Google Cloud products with customer infrastructure. They will also serve as a technical feedback loop to improve Google's AI product roadmap based on real-world field insights.

Detroit, Michigan, United StatesonsiteFULL_TIME

Posted

About the role

What will you do at Google?

MINIMUM QUALIFICATIONS:

* Bachelor’s degree in Engineering, Computer Science, a related field, or

equivalent practical experience.

* 8 years of experience with software development using Python or similar

coding languages.

* Experience building pipelines for structured and unstructured data using both

vector databases and RAG-like architectures to power enterprise AI solutions.

* Experience taking production-grade AI-driven solutions from conception to

launch for customers.

* Experience architecting AI systems on cloud platforms (e.g. Google Cloud

Platform (GCP)).

* Experience leading technical discovery sessions with customers.

PREFERRED QUALIFICATIONS:

* Master’s degree or PhD in AI, Computer Science, or a related technical field.

* Experience implementing multi-agent systems using frameworks (e.g.,

LangGraph, CrewAI, ADK) and complex patterns (e.g., ReAct, self-reflection,

hierarchical delegation).

* Knowledge of Large Language Model (LLM) native metrics (e.g., tokens/sec,

cost-per-request) and techniques for optimizing state management and granular

tracing.

ABOUT THE JOB:

As a Generative AI (GenAI) Forward Deployed Engineer (FDE) at Google Cloud, you

are an embedded builder who bridges the gap between frontier AI products and

production-grade reality within customers. Unlike traditional advisory roles,

you will function as an innovator-builder, moving beyond high-level architecture

to code, debug, and jointly ship bespoke agentic solutions directly within the

customer’s environment. This role is designed for high-agency engineers with a

founder’s mindset. You will address blockers to production including solving the

integration complexities, data readiness issues, and state-management issues

that prevent AI from reaching enterprise-grade maturity. By embedding with

strategic accounts, you will serve a dual purpose: providing white glove

deployment of complex AI systems and acting as a critical feedback loop,

transforming real-world field insights into Google Cloud’s future product

roadmap.

It's an exciting time to join Google Cloud’s Go-To-Market team, leading the AI

revolution for businesses worldwide. You’ll excel by leveraging Google's brand

credibility—a legacy built on inventing foundational technologies and proven at

scale. We’ll provide you with the world's most advanced AI portfolio, including

frontier Gemini models, and the complete Vertex AI platform, helping you to

solve business problems. We’re a collaborative culture providing direct access

to DeepMind's engineering and research minds, empowering you to solve customer

challenges. Join us to be the catalyst for our mission, drive customer success,

and define the new cloud era—the market is yours.

Individual pay is determined by factors including job-related skills,

experience, and relevant education or training.

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google

[https://www.google.com/about/careers/applications/benefits/].

RESPONSIBILITIES:

* Serve as a developer for complex AI applications, transitioning from rapid

prototypes to production-grade agentic workflows (e.g., multi-agent systems,

MCP servers) that drive measurable Return on Investment (ROI).

* Architect and code the connective tissue between Google’s AI products and

customer's live infrastructure, including APIs, legacy data silos, and

security perimeters as part of an expert team.

* Build high-performance evaluation pipelines and observability frameworks to

ensure agentic systems meet rigorous requirements for accuracy, safety, and

latency.

* Identify repeatable field patterns and friction points in Google’s AI stack,

converting them into reusable modules or formal product feature requests for

the Engineering teams.

* Co-build with Customer Engineering teams to instill Google-grade development

best practices, ensuring long-term project success and high end-user

adoption.

Which skills does this role require?

PythonGoogle Cloud PlatformVertex AIVector DatabasesRAG ArchitecturesSoftware DevelopmentSystem ArchitectureData PipelinesObservability FrameworksAPI IntegrationTechnical DiscoveryLLM MetricsGeminiRAGObservabilityAPISoftware EngineeringCloud ComputingEnterprise AICustomer EngineeringLatency OptimizationAgentic WorkflowsMCP ServersDeepMindGCPLLMsPrototypingProduct Strategy

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