Forward Deployed Engineer III, Generative AI, Google Cloud

Google•Pittsburgh, PA

1d•$174,000 - $252,000

About The Position

As a GenAI Forward Deployed Engineer (FDE) at Google Cloud, you will be an embedded builder who bridges frontier AI products and production-grade customer solutions. You will move beyond high-level architecture to code, debug, and jointly ship bespoke agentic solutions directly in customer environments. The role is designed for high-agency engineers with a founder’s mindset. You will address blockers to production, including integration complexities, data readiness, and state management. By embedding with accounts, you will provide white-glove deployment of AI systems and turn real-world field insights into feedback for Google Cloud’s future product roadmap. You will join Google Cloud’s Go-To-Market team, working with Google’s AI portfolio, including Gemini models and the Vertex AI platform, and collaborating with teams that have access to DeepMind engineering and research expertise. Individual pay is determined by job-related skills, experience, and relevant education or training. The US compensation range is $174,000–$252,000, plus a 15% bonus target, equity, and benefits.

Requirements

Bachelor’s degree in Engineering, Computer Science, or 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, such as 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 vector databases and retrieval-augmented generation (RAG)-like architectures to power enterprise AI solutions.

Nice To Haves

Master’s degree or PhD in AI, Computer Science, or a related technical field.
Experience implementing multi-agent systems using frameworks such as LangGraph, CrewAI, or ADK, and patterns such as ReAct, self-reflection, or hierarchical delegation.
Knowledge of LLM-native metrics, such as tokens per second and cost per request, and techniques for optimizing state management and granular tracing.

Responsibilities

Serve as a developer for complex AI applications, moving rapid prototypes to production-grade agentic workflows, such as multi-agent systems and model context protocol (MCP) servers, that drive measurable return on investment.
Architect and code integrations between Google’s AI products and customers’ 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 requirements for accuracy, safety, and latency.
Identify repeatable field patterns and friction points in Google’s AI stack and convert them into reusable modules or formal product feature requests for Engineering teams.
Co-build with customer engineering teams to establish Google-grade development best practices and support long-term project success and high end-user adoption.