Staff Forward Deployed Engineer, Developer AI, Google Cloud
- Compensation
- Not disclosed
- Location
- Sydney NSW, Australia; Melbourne VIC, Australia
- Work setup
- Not specified
- Seniority
- Staff
- Employment
- Not specified
- Experience
- 8+ years
- Category
- Solutions engineering
- Posted
Skills
Must have
- 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 deploying, scaling, and debugging Large Language Model (LLM) or agent-based systems in production environments (including tools, memory, orchestration, evaluation, tracing, and cost/latency
- Experience with end-to-end technical ownership of engineering projects with executive stakeholders.
Nice to have
- 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 with agentic frameworks and harness layers, such as Google's Agent Development Kit (ADK) or equivalent, protocol-level interoperability (MCP, A2A) across third-party ISV platforms (e.g., At
- Knowledge of "LLM-native" metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.
Full description
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. 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 road map.
In this role, you will embed with the engineering organizations of our largest customers and take Google's developer AI - Gemini Code Assist, anti-gravity IDE, SDK, CLI and the agentic surfaces beneath them - from the first conversation to a production-grade workflow. You find what actually slows an engineering organization down, design the system that fixes it, and own it end-to-end: discovery, build, roll-out, hardening, and the long tail of making it reliable.
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.
- 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 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.
- Design and deploy production-grade agentic developer workflows on Google Cloud's AI stack, executing large-scale refactors, language migrations, spec-to-PR pipelines, and automated review/incident-to-fix loops.
- Embed with customers' Staff Engineers and Leaders to identify core SDLC bottlenecks, such as legacy migrations, test coverage, review latency, or onboarding friction, and define rigorous success metrics.