Forward Deployed Engineering Manager, Generative Ai, Google Cloud

Google Google · Big Tech · Tokyo, Japan

Manager for a Forward Deployed Engineering team focused on deploying bespoke agentic AI solutions within customer environments on Google Cloud. The role involves technical leadership, team management, hiring, and ensuring the team can resolve production-level obstacles related to AI integration, data readiness, and state management. Emphasis on designing and deploying multi-agent workflows, RAG systems, and tool-calling capabilities.

What you'd actually do

  1. Serve as the technical lead, establish 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 opportunities and deploy specialized experts (MLOps, GenMedia, or Agentic systems) to key accounts.
  3. Lead technical hiring for the forward deployed engineering team, evaluate AI/ML, systems engineering, and coding skills to build an excellent engineering team.
  4. Identify skill gaps in emerging tech (MCP, tool-calling, and foundation models), and ensure the team maintains subject-matter-expertise in an evolving AI stack.
  5. Collaborate with product and engineering teams to resolve blockers and translate field insights into road maps while building internal tools to drive organizational efficiency.

Skills

Required

  • Python
  • Experience in developing AI/Generative AI solutions utilizing AI tools
  • designing multi-agent workflows and Retrieval-Augmented Generation (RAG) systems
  • Experience in architecting AI solutions within infrastructures
  • Experience in designing interfaces for AI and agentic systems
  • Experience in designing secure, observable multi-agent systems using design patterns (ReAct, self-reflection, etc.), state management, and tool-calling protocols

Nice to have

  • Master's degree in Computer Science, Engineering, or a related technical field
  • prioritizing context engineering, transparency, and explainability to foster user trust
  • ensuring data sovereignty and secure governance
  • Ability to perform deep discovery interviews to find the business problem and translate hardware/AI constraints for C-suites and technical teams

What the JD emphasized

  • deploy bespoke agentic solutions directly within customer environments
  • resolve production-level obstacles, including data readiness issues, integration complexities, and state-management challenges
  • designing multi-agent workflows and Retrieval-Augmented Generation (RAG) systems
  • design secure, observable multi-agent systems using design patterns (ReAct, self-reflection, etc.), state management, and tool-calling protocols

Other signals

  • leading a team that deploys bespoke agentic solutions
  • empower and unblock your team as they resolve production-level obstacles
  • deploy specialized experts (MLOps, GenMedia, or Agentic systems) to key accounts
  • designing multi-agent workflows and Retrieval-Augmented Generation (RAG) systems
  • design secure, observable multi-agent systems using design patterns (ReAct, self-reflection, etc.), state management, and tool-calling protocols