As a GenAI Forward Deployed Engineer at Google Cloud, you will be an embedded builder who bridges the gap between frontier Artificial Intelligence (AI) products and production-grade reality within customer environments. Unlike traditional advisory roles, you will function as a "builder-consultant," moving beyond architecture to code, debug, and jointly ship bespoke agentic solutions.
This role is designed for high-agency engineers with a founder’s mindset. You will solve the integration complexities, data readiness, 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 and acting as a critical feedback loop for Google Cloud’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.
The US base salary range for this full-time position is $183,000-$265,000 + 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
- Lead the "Discovery-to-Deployment" journey, serving as the lead developer for AI applications and transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems and MCP servers) that drive measurable ROI.
- Bridge the enterprise gap by architecting and coding the "connective tissue" between Google’s AI products and customer's live infrastructure, including APIs, legacy data silos, and security perimeters.
- Engineer for production excellence, building high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet rigorous requirements for accuracy, safety, and latency.
- Act as a product catalyst, identifying repeatable field patterns and technical "friction points" in Google’s AI stack and converting them into reusable modules or product feature requests for the Engineering teams.
- Drive regional leadership and upskilling by mentoring talent, co-building with customer teams, and influencing cross-functional strategies to uplevel organizational technical capabilities.
Qualifications
Minimum qualifications:
- Bachelor’s degree in Science, Technology, Engineering, Mathematics, a related technical field, or equivalent practical experience.
- 8 years of experience shipping production-grade AI-driven solutions to external or internal customers.
- Experience in Python.
- Experience 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 AI, Computer Science, or a related technical field.
- Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, or Google’s ADK) and patterns like ReAct, self-reflection, and hierarchical delegation.
- Knowledge of "LLM-native" metrics (tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.
- Ability to implement secure agentic workflows incorporating MCP, tool-calling, and OAuth-based authentication.