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Open nowFirst seen 2 hours ago

Forward Deployed Engineering Manager, Applied AI, Google Cloud

Google3,474 open roles

Where
Singapore
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Your applicationOpen nowForward Deployed Engineering Manager, Applied AI, Google CloudGoogle · Singapore

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The clock on this job

Early applications get read.

12.4% of postings close within 7 days. Measured by our own scanner across the market. Google postings stay open a median of 29 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.4%7 days
  4. 20.0%14 days
  5. 35.5%30 days
  6. 58.6%60 days
This job: first seen 2 hours ago

Google median: 29 days open

The posting

Google will be prioritizing applicants who have a current right to work in Singapore, and do not require Google's sponsorship of a visa.

Minimum qualifications:

  • 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 in Python or similar coding languages.
  • Experience developing and deploying agentic solutions utilizing tools, multi-agent workflows and RAG systems.
  • Experience with customer projects and resource management.

Preferred qualifications:

  • Master’s degree or PhD in AI, Computer Science, or a related technical field.
  • 2 years of experience managing a software engineering, FDE, or similar technical customer-facing team in a cloud computing environment.
  • Experience designing end-to-end secure, observable multi-agent systems using complex design patterns (e.g., ReAct, self-reflection), state management, and tool-calling protocols.
  • Experience designing intuitive interfaces for complex AI and agentic systems, prioritizing context engineering, transparency, and explainability to foster user trust.
  • Experience architecting AI solutions within complex infrastructures, ensuring data sovereignty and secure governance.
  • Experience performing discovery interviews to identify business problems and translate complex hardware/AI constraints for C-suites and technical teams.

About the job

As the Manager of an Applied AI 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. 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 (Agentic systems in Customer Experience) to key accounts.
  • Lead technical hiring for Applied AI FDE, evaluating AI Agent expertise, systems engineering, and coding skills to build an exceptional engineering team.
  • 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 while building internal tools to drive organizational efficiency.
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