As a Manager of a GenAI Forward Deployed Engineering (FDE) team, you will lead a squad of AI/ML engineers who bridge the gap between frontier AI products and production-grade reality within customers. You are responsible for a team that doesn't just consult, but codes, debugs and jointly deploys bespoke agentic solutions directly within customer environments. In this role, you will provide deep-dive technical mentorship to your team while balancing high-level strategic alignment with Product, Engineering, and Google Cloud Regional Sales leadership. Your mission is to empower and unblock your team as they resolve production-level obstacles, including data readiness issues, integration complexities, and state-management challenges that hinder AI from achieving enterprise-grade maturity.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.
Minimum qualifications:
- Bachelor's degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience.
- 8 years of experience in cloud computing or a technical customer-facing role.
- 2 years of experience managing a software engineering, forward deployed engineering, or a similar technical customer-facing team in a cloud computing environment.
- Experience in machine learning and in developing AI/Generative AI solutions utilizing AI tools and designing multi-agent workflows and retrieval-augmented generation (RAG) systems.
Preferred qualifications:
- Master’s degree or PhD in AI, Computer Science, or a related technical field.
- Background in architecting AI solutions within complex infrastructures, ensuring data sovereignty and secure governance.
- Expertise in designing intuitive interfaces for complex AI and agentic systems, prioritizing context engineering, transparency, and explainability to foster user trust.
- Ability to design end-to-end secure, observable multi-agent systems using complex design patterns (e.g., ReAct, self-reflection), state management, and tool-calling protocols.
- Ability to perform deep ‘discovery’ interviews to find the true business problem and translate complex hardware/AI constraints for C-suites and deep-technical teams.