Forward Deployed Engineering Manager, Genai, Google Cloud

Google Google · Big Tech · Singapore

Manager of a GenAI Forward Deployed Engineering (FDE) team responsible for leading AI/ML engineers to deploy bespoke agentic solutions within customer environments. The role involves technical mentorship, strategic alignment with Product, Engineering, and Sales, and resolving production-level obstacles for AI adoption in enterprise settings.

What you'd actually do

  1. Serve as the ultimate technical lead, establishing code standards, architectural best practices, and benchmarks to elevate engineering excellence across the team.
  2. Partner with sales and tech leadership to define requirements for high-value opportunities, deploying specialized experts (MLOps, GenMedia, or Agentic systems) to key accounts.
  3. 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.
  4. 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.
  5. Collaborate with product and engineering to resolve blockers and translate field insights into roadmaps, building internal tools to drive organizational efficiency.

Skills

Required

  • 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 developing AI/Generative AI (GenAI) solutions utilizing AI tools and designing multi-agent workflows and retrieval-augmented generation (RAG) systems.

Nice to have

  • 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.

What the JD emphasized

  • deploying bespoke agentic solutions directly within customer environments
  • resolve production-level obstacles
  • data readiness issues
  • integration complexities
  • state-management challenges
  • designing multi-agent workflows
  • retrieval-augmented generation (RAG) systems
  • architecting AI solutions within complex infrastructures
  • designing end-to-end secure, observable multi-agent systems using complex design patterns (e.g., ReAct, self-reflection), state management, and tool-calling protocols

Other signals

  • leading a team of AI/ML engineers
  • deploying bespoke agentic solutions directly within customer environments
  • mentorship and strategic alignment with Product, Engineering, and Sales leadership
  • resolving production-level obstacles including data readiness, integration, and state-management