Forward Deployed Engineering Manager, Generative Ai, Public Sector and Education, Google Cloud

Google Google · Big Tech · Gurugram, Haryana, India

Lead a team of AI/ML engineers focused on deploying bespoke agentic solutions within customer environments for Google Cloud's Public Sector and Education clients. This role involves technical mentorship, strategic alignment with sales and product leadership, and resolving production-level obstacles to achieve enterprise-grade AI maturity. The team leverages Google's frontier AI models and Vertex AI platform to solve complex business problems.

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 (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 (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 while building internal tools to drive organizational efficiency.

Skills

Required

  • Bachelor’s degree in Engineering, Computer Science, a related field, 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 (FDE), or similar technical customer-facing team in a cloud computing environment.
  • Experience developing AI/GenAI solutions utilizing AI tools, or designing multi-agent workflows or Retrieval-augmented generation (RAG) systems.
  • Experience in Python or similar coding language.

Nice to have

  • Master’s or PhD in AI, Computer Science, or a related technical field.
  • Experience designing secure, observable multi-agent systems using complex design patterns (e.g., ReAct, self-reflection), state management, and tool-calling protocols.
  • Experience working with the Public Sector and Education.
  • 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

  • deploy bespoke agentic solutions
  • resolve production-level obstacles
  • state-management challenges
  • enterprise-grade maturity
  • tool-calling
  • multi-agent workflows
  • multi-agent systems
  • tool-calling protocols

Other signals

  • leading AI/ML engineers
  • deploy bespoke agentic solutions
  • technical mentorship
  • resolve production-level obstacles
  • data readiness issues
  • integration complexities
  • state-management challenges
  • enterprise-grade maturity
  • frontier Gemini models
  • Vertex AI platform
  • solve customer challenges
  • code standards
  • architectural best practices
  • deploying specialized experts
  • MLOps
  • GenMedia
  • Agentic systems
  • technical hiring
  • AI/ML expertise
  • systems engineering
  • coding skills
  • skill gaps in emerging tech
  • tool-calling
  • foundation models
  • subject matter expertise
  • evolving AI stack
  • translate field insights into road maps
  • building internal tools
  • organizational efficiency
  • cloud computing
  • technical customer-facing role
  • managing a software engineering
  • Forward Deployed Engineering (FDE)
  • technical customer-facing team
  • cloud computing environment
  • developing AI/GenAI solutions
  • utilizing AI tools
  • designing multi-agent workflows
  • Retrieval-augmented generation (RAG) systems
  • Python
  • designing secure, observable multi-agent systems
  • complex design patterns
  • ReAct
  • self-reflection
  • state management
  • tool-calling protocols
  • Public Sector and Education
  • designing intuitive interfaces
  • complex AI and agentic systems
  • context engineering
  • transparency
  • explainability
  • foster user trust
  • architecting AI solutions
  • complex infrastructures
  • data sovereignty
  • secure governance
  • discovery interviews
  • identify business problems
  • translate complex hardware/AI constraints
  • C-suites
  • technical teams