Forward Deployed Engineering Manager, GenAI, Google Cloud

Minimum qualifications:

• Bachelor's degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience.
• 10 years of experience in cloud computing or a technical customer-facing role, with experience in Python.
• 2 years of experience managing a software engineering, forward deployed engineering, or a similar technical customer-facing team in a cloud computing environment.
• Experience developing AI/Generative AI solutions utilizing AI tools and designing multi-agent workflows and RAG systems.
• People management experience.

Preferred qualifications:

• Master’s degree or PhD in AI, Computer Science, or a related technical field.
• Experience 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 (ReAct, self-reflection, etc.), state management, and tool-calling protocols.

About the job

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 AI products and production-grade reality within customers. You will be 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 technical mentorship to your team, managing 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.

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 $234,000-$325,000 + 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

• Serve as the ultimate technical lead, establishing code standards, architectural best practices, and benchmarks to elevate engineering excellence across the team.
• Partner with sales and tech leadership to define requirements for high-value opportunities, deploying specialized experts (MLOps, GenMedia, or Agentic systems) to key accounts.
• Lead technical hiring for forward deployed engineering, evaluating AI/ML expertise, systems engineering, and coding skills to build an engineering squad.
• Identify skill gaps in emerging tech (MCP, tool-calling, and foundation models), ensuring the team maintains subject matter expertise in an evolving AI stack.
• Collaborate with product and engineering to resolve blockers and translate field insights into roadmaps, building internal tools to drive organizational efficiency.