Forward Deployed Engineering Manager, GenAI, Delta, Google Cloud
Summary
Lead a team of AI/ML engineers to deploy bespoke generative AI solutions directly in customer environments, balancing technical mentorship, strategic alignment, and hands-on coding to solve enterprise-grade challenges.
Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
- Serve as the ultimate 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 (MLOps, GenMedia, or agentic systems) to key accounts.
- Lead technical hiring for forward deployed engineering, evaluating Artificial Intelligence/Machine Learning (AI/ML) expertise, systems engineering, and coding skills to build an engineering squad.
- 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, building internal tools to drive organizational efficiency.
Minimum qualifications:
- Bachelor's degree in Science, Technology, Engineering, Mathematics, 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, or a similar technical customer-facing team in a cloud computing environment.
- Experience developing AI/Generative AI (GenAI) solutions utilizing AI tools and designing multi-agent workflows and retrieval-augmented generation (RAG) systems.
- Experience in people management, including leading technical teams.
Preferred qualifications:
- Master’s degree or PhD in AI, Computer Science, or a related technical field.
- Expertise in designing intuitive interfaces for complex AI and agentic systems, prioritizing context engineering, transparency, and explainability to foster user trust.
- Background in architecting AI solutions within complex infrastructures, ensuring data sovereignty and secure governance.
- Ability to design end-to-end secure, observable multi-agent systems using complex design patterns (e.g., ReAct, self-reflection), state management, and tool-calling protocols.
- Ability to perform deep discovery interviews to find the true business problem and translate complex hardware/AI constraints for C-suites and deep-technical teams.