Forward Deployed Engineering Manager, Ai, Google Cloud

Google Google · Big Tech · Singapore

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

What you'd actually do

  1. Serve as the 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

  • Python or similar coding languages
  • developing AI/GenAI solutions utilizing AI tools
  • designing multi-agent workflows or RAG systems
  • cloud computing
  • technical customer-facing role
  • managing a software engineering, FDE, or similar technical customer-facing team

Nice to have

  • Master’s degree or PhD in AI, Computer Science, or a related technical field
  • industry agentic transformation
  • designing end-to-end secure, observable multi-agent systems using complex design patterns (e.g., ReAct, self-reflection), state management, and tool-calling protocols
  • designing intuitive interfaces for complex AI and agentic systems, prioritizing context engineering, transparency, and explainability
  • architecting AI solutions within complex infrastructures, ensuring data sovereignty and secure governance
  • performing discovery interviews to identify business problems and translate complex hardware/AI constraints for C-suites and 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
  • tool-calling
  • foundation models
  • multi-agent workflows
  • secure, observable multi-agent systems
  • tool-calling protocols

Other signals

  • leading a team of AI/ML engineers
  • deploying bespoke agentic solutions
  • mentorship and technical leadership
  • resolving production-level obstacles
  • driving customer success with AI