Job Description

Minimum qualifications:

• Bachelor’s degree in Science, Technology, Engineering, Mathematics, a related technical field, or equivalent practical experience.
• 8 years of experience in providing production-grade AI solutions to external or internal customers with L400-level in Python, and architecting AI systems on cloud platforms.
• Experience leading technical discovery sessions with executive stakeholders (C-suite) and engineering teams to define AI and hardware infrastructure requirements.
• Experience building full-stack solutions that interface with enterprise systems.

Preferred qualifications:

• Master's degree or PhD in Computer Science, AI, Machine Learning, or a related technical field.
• Experience in implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, or Google’s Agent Development Kit (ADK)) and patterns like ReAct, self-reflection, and 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.
• Ability to implement secure agentic workflows incorporating Model Context Protocol (MCP), tool-calling, and Open Authorization (OAuth)-based authentication.

About the job

The Google Cloud Platform team helps customers transform and build what's next for their business — all with technology built in the cloud. Our products are developed for security, reliability and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping our customers — developers, small and large businesses, educational institutions and government agencies — see the benefits of our technology come to life. As part of an entrepreneurial team in this rapidly growing business, you will play a key role in understanding the needs of our customers and help shape the future of businesses of all sizes use technology to connect with customers, employees and partners.

As a 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.

In this role, you will need to be a high-agency engineer 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 accounts, you will serve a dual purpose: providing white-glove deployment of AI systems and acting as a critical feedback loop, transforming real-world field insights into Google Cloud’s future product roadmap.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

Responsibilities

• Serve as the lead developer for Artificial Intelligence (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 Artificial Intelligence (AI) products and customer's live infrastructure, including Application Programming Interfaces (APIs), legacy data silos, and security perimeters.
• Build high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet requirements for accuracy, safety, and latency.
• Identify repeatable field patterns and technical 'friction points' in Google’s AI stack, converting them into reusable modules or formal product feature requests for the Engineering teams.