Forward Deployed Engineering Manager, Gen Ai, Manufacturing and Conglomerate, Google Cloud

Google Google · Big Tech · Mumbai, Maharashtra, India

Manager of a GenAI Forward Deployed Engineering (FDE) team leading AI/ML engineers to deploy bespoke agentic solutions within customer environments. Responsibilities include technical mentorship, resolving production obstacles, and collaborating with Product, Engineering, and Sales leadership. The role focuses on bridging the gap between frontier AI products and production-grade reality for enterprise customers.

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 (e.g., MLOps, GenMedia, or Agentic systems) to key accounts.
  3. Lead technical hiring for FDE, evaluating AI/ML expertise, systems engineering, and coding skills to build an exceptional engineering team.
  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 while building internal tools to drive organizational efficiency.

Skills

Required

  • Python or similar coding language
  • Experience developing AI/GenAI solutions utilizing AI tools, or designing multi-agent workflows or Retrieval-Augmented Generation (RAG) systems.

Nice to have

  • Master’s degree or PhD in AI, Computer Science, or a related technical field.
  • Experience designing end-to-end secure, observable multi-agent systems using complex design patterns (e.g., ReAct, self-reflection), state management, and tool-calling protocols.
  • Experience working with manufacturing and conglomerates customers.
  • Experience designing intuitive interfaces for complex AI and agentic systems, prioritizing context engineering, transparency, and explainability to foster user trust.
  • Experience architecting AI solutions within complex infrastructures, ensuring data sovereignty and secure governance.
  • Experience 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
  • resolve production-level obstacles
  • data readiness issues
  • integration complexities
  • state-management challenges
  • enterprise-grade maturity
  • tool-calling
  • foundation models
  • multi-agent systems
  • tool-calling protocols

Other signals

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