Senior Staff Forward Deployed Engineer, GenAI, Google Cloud

Google; Lavamünd; Vor Ort; EUR 150.000 - 190.000; Vollzeit

Zusammenfassung

Google Cloud is seeking a GenAI Forward Deployed Engineer to embed with strategic accounts, bridging frontier AI products and production-grade reality. You will code, debug, and ship bespoke agentic solutions directly within customer environments, tackling integration, data readiness, and state-management challenges to drive enterprise adoption.

You will join Google's Go-To-Market AI leadership, leveraging Gemini and Vertex AI, collaborating with DeepMind engineers to solve customer challenges

Qualifikationen

• Bachelor's degree in Engineering, Computer Science, a related field, or equivalent practical experience.
• 8 years of experience in cloud computing or a technical customer-facing role.
• Experience taking production-grade AI-driven solutions from conception to launch and architecting AI systems on cloud platforms (e.g., Google Cloud Platform (GCP)).
• Experience building pipelines for structured and unstructured data using both vector databases and Retrieval-Augmented Generation (RAG)-like architectures to power enterprise AI solutions.
• Experience leading technical discovery sessions.

Aufgaben

• Serve as a developer for complex AI applications, transitioning from rapid prototypes to production-grade agentic workflows that drive ROI.
• Architect and code the connective tissue between Google's AI products and the customer's live infrastructure, including APIs and data silos.
• Build high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet accuracy, safety, and latency requirements.
• Identify repeatable field patterns and friction points in Google's AI stack and translate them into reusable modules or product feature requests.
• Co-build with customer engineering teams to instill Google-grade development best practices for long-term success.

Kenntnisse

Cloud computing

Customer-facing

AI deployment

Data pipelines

Vector databases

RAG architectures

Technical discovery

Production-grade AI

Ausbildung

Bachelor's degree in Engineering/CS/related field

Master's or PhD in AI/CS or related field

Tools

Google Cloud Platform (GCP)

LangGraph

CrewAI

ADK

Model Context Protocol (MCP)

Jobbeschreibung

info_outlineXIn most instances, this position requires in-person interviews as part of the hiring process.

Minimum qualifications

• Bachelor's degree in Engineering, Computer Science, a related field, or equivalent practical experience.
• 8 years of experience in cloud computing or a technical customer-facing role.
• Experience taking production-grade AI-driven solutions from conception to launch and architecting AI systems on cloud platforms (e.g., Google Cloud Platform (GCP).)
• Experience building pipelines for structured and unstructured data using both vector databases and Retrieval-Augmented Generation (RAG)-like architectures to power enterprise AI solutions.
• Experience leading technical discovery sessions.

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).
• Experience identifying business problems and translating hardware/AI constraints for C-suites and technical teams.
• Experience influencing and driving outcomes with cross-functional teams on engagements of high complexity and large scope and scale.
• Experience performing discovery interviews to identify business problems and translate hardware/AI constraints for C-suites and technical teams.
• 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 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. Your role is designed for high-agency engineers with a founder's mindset as 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'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.

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 (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.