Forward Deployed Engineering Manager, Genai, (mandarin, English)

Google Google · Big Tech · Singapore

Lead a team of AI/ML engineers to deploy bespoke agentic solutions within customer environments, providing technical mentorship and strategic alignment with Product, Engineering, and Sales leadership. Resolve production-level obstacles and identify skill gaps in emerging AI technologies.

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 business and tech leadership to define requirements for high-value opportunities, deploying specialized experts (e.g., Machine Learning Operations (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 (e.g., 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 road maps, building internal tools to drive organizational efficiency.

Skills

Required

  • Experience developing AI/Generative AI (GenAI) solutions utilizing AI tools
  • designing multi-agent workflows
  • Retrieval-Augmented Generation (RAG) systems
  • leading AI solutions from conception to launch for customers
  • technical discovery sessions with China-go-Global customers
  • communicate in Mandarin and English fluently

Nice to have

  • Master’s degree or PhD in AI, Computer Science, or a related technical field.
  • Experience in designing intuitive interfaces for complex AI and agentic systems, prioritizing context engineering, transparency, and explainability to foster user trust.
  • Experience in architecting AI solutions within complex infrastructures, ensuring data sovereignty and secure governance.
  • 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.
  • Ability to design secure, observable multi-agent systems using complex design patterns (e.g., ReAct, self-reflection), state management, and tool-calling protocols.

What the JD emphasized

  • deploy bespoke agentic solutions directly within customer environments
  • resolve production-level obstacles
  • data readiness issues
  • integration complexities
  • state-management challenges
  • tool-calling

Other signals

  • leading a team that codes, debugs and jointly deploys bespoke agentic solutions directly within customer environments
  • resolve production-level obstacles, including data readiness issues, integration complexities, and state-management challenges
  • partner with business and tech leadership to define requirements for high-value opportunities
  • deploying specialized experts (e.g., Machine Learning Operations (MLOps), GenMedia, or Agentic systems) to key accounts
  • identify skill gaps in emerging tech (e.g., Model Context Protocol (MCP), tool-calling, and foundation models)