About the job
As a Manager of a GenAI Forward Deployed Engineering (FDE) team, you will lead 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. 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.
Responsibilities
- Serve as the 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 Artificial Intelligence/Machine Learning (AI/ML) expertise, systems engineering, and coding skills to build an engineering squad.
- Identify skill gaps in emerging tech (Model Context Protocol (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.
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