Forward Deployed Engineer III, Generative AI, Google Cloud
- Location
- Onsite (San Francisco, CA · Atlanta, GA · Boulder, CO · Cambridge, MA · Chicago, IL · Addison, TX · Detroit, MI · Irvine, CA · Kirkland, WA · Los Angeles, CA · Miami, FL · Mountain View, CA · New York, NY · Portland, OR · Reston, VA · San Diego, CA · Seattle, WA · Sunnyvale, CA · Washington, DC)
- Compensation
- $174k - $252k/yr
- Employment
- Full-time
- Level
- Senior Level
About the Role
As a GenAI Forward Deployed Engineer at Google Cloud, you will be an embedded builder who bridges the gap between frontier AI products and production-grade reality within customers. 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.
Skills
Benefits
- Bonus
- Equity
- Benefits
Full job details
Minimum qualifications:
- Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
- 5 years of experience with software development using Python or similar coding languages.
- Experience architecting AI systems on cloud platforms (e.g., GCP).
- Experience taking production-grade AI-driven solutions from conception to launch for customers.
- Experience leading technical discovery sessions with customers.
- Experience building pipelines for structured and unstructured data using both vector databases and retrieval-augmented generation (RAG)-like architectures to power enterprise AI solutions.
Preferred qualifications:
- Master’s or PhD in AI, Computer Science, or a related technical field.
- Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, ADK) and 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.
About the job:
As a GenAI Forward Deployed Engineer (FDE) at Google Cloud, you will be 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.
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.
The US base salary range for this full-time position is $174,000-$252,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or 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, model context protocol (MCP) servers) that drive measurable return on investment.
- 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.
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