Forward Deployed Engineering Manager, Genai, Delta, Google Cloud

Google Google · Big Tech · Singapore

Manager of GenAI Forward Deployed Engineering (FDE) team leading AI/ML engineers to deploy bespoke agentic solutions in customer environments, providing technical mentorship and resolving production-level obstacles related to data, integration, and state management to achieve enterprise-grade maturity.

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.
  • Experience in people management, including leading technical teams.

Nice to have

  • Master’s degree or PhD in AI, Computer Science, or a related technical field.
  • Expertise in designing intuitive interfaces for complex AI and agentic systems, prioritizing context engineering, transparency, and explainability to foster user trust.
  • Background in architecting AI solutions within complex infrastructures, ensuring data sovereignty and secure governance.
  • 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

  • lead a squad of AI/ML engineers
  • deploy bespoke agentic solutions
  • resolve production-level obstacles
  • data readiness issues
  • integration complexities
  • state-management challenges
  • enterprise-grade maturity
  • tool-calling
  • foundation models
  • multi-agent workflows
  • retrieval-augmented generation (RAG) systems
  • design end-to-end secure, observable multi-agent systems
  • tool-calling protocols

Other signals

  • leading AI/ML engineers
  • deploying bespoke agentic solutions
  • customer environments
  • technical mentorship
  • production-level obstacles
  • data readiness issues
  • integration complexities
  • state-management challenges
  • enterprise-grade maturity