Lead GenAI Forward Deployed Engineer, YouTube GTM Operations

About the role

In gTech Users and Products (gUP), our mission is to advocate for Google’s users by creating helpful and trusted experiences across the product ecosystem. We achieve this by meeting partners and consumers where they are with support and help, representing their needs with our product partners and proposing fixes and features that elevate their engagement with Google's diverse product ecosystem. Additionally we provide a range of product services that ensure our products are optimized for every user, no matter where they are in the world (e.g., localization, digitization, partner integration, and more).

It's an exciting time to join YouTube's Go To Market Organization's AI Accelerator team, devising the AI transformation for YouTube Business GTM operations. The AI Accelerator is a newly formed task group dedicated to driving AI transformation across the organization. Our mission is to operate as a high-velocity, horizontal transformation engine, partnering directly with YouTube Biz GTM business domains to fundamentally redesign legacy workflows from scratch and leverage applied AI to drive direct business impact for YouTube. We operate at the intersection of consulting, product strategy, applied AI and systems engineering. We identify the most painful operational bottlenecks across the organization and rapidly deploy intelligent, enterprise-grade AI powered solutions to solve them.

Responsibilities

• Build GenAI PoCs (LLMs, RAG, agentic frameworks) to demonstrate feasibility to stakeholders, translating ambiguous business problems into software solutions.
• Serve as engineering Lead delivering complex AI applications, transitioning rapid prototypes to production-grade systems (e.g., multi-agent systems, MCP servers) that drive Return on Investment (ROI).
• Act as a trusted engineering partner to product managements and stakeholders, co-creating tool roadmaps that enable YouTube's business operations.
• Author technical designs, write clean/maintainable code, build front-ends, and deliver from scoping to deployment.
• Build high-performance eval pipelines and observability frameworks to resolve AI bottlenecks (data readiness, system integration, state-management) while ensuring accuracy, safety, compliance, and latency.