Forward Deployed Engineer V, Generative AI, Cloud

Forward Deployed Engineer V, Generative AI, Google Cloud

_corporate_fare_ Google _place_ San Francisco, CA, USA;
Atlanta, GA, USA; +6 more; +5 more

** Advanced*
* Experience owning outcomes and decision making, solving ambiguous problems and influencing stakeholders; deep expertise in domain.

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XApplicants in San Francisco:
Qualified applications with arrest or conviction records will be considered for employment in accordance with the San Francisco Fair Chance Ordinance for Employers and the California Fair Chance Act.

Note:

By applying to this position you will have an opportunity to share your preferred working location from the following:
** San Francisco, CA, USA;
Atlanta, GA, USA;
Austin, TX, USA;
Chicago, IL, USA;
New York, NY, USA;
Reston, VA, USA;
Sunnyvale, CA, USA** .

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* Minimum qualifications:

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* + 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 taking production-grade AI-driven solutions from conception to launch.

+ Experience building pipelines for structured and unstructured data using both vector databases and RAG-like architectures to power enterprise AI solutions.

+ Experience leading technical discovery sessions with customers.

+ Experience architecting AI systems on cloud platforms (e.g., GCP).

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* Preferred qualifications:

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

+ Experience implementing multi-agent systems using frameworks (e.g., Lang Graph, CrewAI, ADK) and complex patterns (e.g., ReAct, self-reflection, hierarchical delegation).

+ Knowledge of "LLM-native" metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.

+ Experience in a post-sales or technical consulting delivery function.

** About the job*
* As a GenAI Forward Deployed Engineer (FDE) in the Google Cloud Consulting organization, you are an embedded builder who bridges the gap between frontier AI products and production-grade reality within customers. Unlike traditional advisory roles, you 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 challenges that prevent AI from reaching enterprise-grade maturity. By embedding with strategic accounts, you 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 '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 Deep Mind'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: $262000 - $365000 (USD) + 25% bonus target + equity + benefits

Learn more about benefits at Google () .

** 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 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 pre-sales and product teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.

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