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, strategic alignment, and resolving production-level obstacles related to data readiness, integration, and state management 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 (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
  • developing AI/GenAI solutions utilizing AI tools
  • designing multi-agent workflows or Retrieval-Augmented Generation (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
  • designing end-to-end secure, observable multi-agent systems using complex design patterns (e.g., ReAct, self-reflection), state management, and tool-calling protocols
  • working with manufacturing and conglomerates customers
  • 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
  • resolve production-level obstacles
  • data readiness issues
  • integration complexities
  • state-management challenges
  • AI from achieving 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