Forward Deployed Engineering Manager, Google Cloud Consulting

Google Google · Big Tech · London, United Kingdom

Manager for a Forward Deployed Engineering (FDE) team within Google Cloud Consulting, leading AI/ML engineers to deploy bespoke agentic solutions in customer environments. Responsibilities include technical mentorship, hiring, identifying skill gaps in AI technologies, and collaborating with product/engineering teams. Requires experience in cloud computing, managing technical teams, and developing AI/Generative AI solutions including multi-agent workflows and RAG systems.

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 (Machine Learning Operations (MLOps), Generative Media (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 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

  • Python
  • cloud computing
  • technical customer-facing role
  • managing a software engineering team
  • managing a forward deployed engineering team
  • managing a technical customer-facing team
  • developing AI/Generative AI solutions
  • designing multi-agent workflows
  • designing RAG systems
  • people management

Nice to have

  • Master’s degree or PhD in AI, Computer Science, or a related technical field
  • architecting AI solutions within complex infrastructures
  • ensuring data sovereignty and secure governance
  • designing intuitive interfaces for complex AI and agentic systems
  • prioritizing context engineering, transparency, and explainability
  • design end-to-end secure, observable multi-agent systems
  • complex design patterns (ReAct, self-reflection, etc.)
  • state management
  • tool-calling protocols

What the JD emphasized

  • AI/ML engineers
  • agentic solutions
  • production-level obstacles
  • AI/Generative AI solutions
  • multi-agent workflows
  • RAG systems
  • tool-calling
  • foundation models
  • multi-agent systems

Other signals

  • leading a team of AI/ML engineers
  • deploying bespoke agentic solutions
  • resolving production-level obstacles
  • technical hiring for FDE
  • architecting AI solutions