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

Minimum qualifications:

• 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.
• 5 years of experience managing a software engineering, FDE, or similar technical customer-facing team in a cloud computing environment.
• Experience in Python or similar coding language.
  
• Experience developing AI/GenAI solutions utilizing AI tools, or designing multi-agent workflows or Retrieval-Augmented Generation (RAG) systems.

Preferred qualifications:

• 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.

About the job

Responsibilities

• Serve as the technical lead, establishing code standards, architectural best practices, and benchmarks to elevate engineering excellence across the team.
• 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.
• Lead technical hiring for FDE, evaluating AI/ML expertise, systems engineering, and coding skills to build an exceptional engineering team.
• 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.
• Collaborate with Product and Engineering to resolve blockers and translate field insights into roadmaps while building internal tools to drive organizational efficiency.