Lead GenAI Forward Deployed Engineer, YouTube GTM Operations

About the role

MINIMUM QUALIFICATIONS:

• Bachelor's degree in Computer Science, Electrical Engineering, Math, a

related quantitative field, or equivalent practical experience in software

development.

• 9 years of experience building APIs or web applications, designing scalable

microservice or system architectures, including backend data pipelines,

distributed systems, and data privacy.

• 8 years of experience in full-stack development using one or more programming

languages (e.g., Python, Go, Java, C++, or TypeScript) with practical

experience building enterprise-grade applied AI solutions.

• 4 years of experience working with teams and business stakeholders to create

product roadmaps.

• 4 years of experience as a Tech Lead or Engineering Manager.

PREFERRED QUALIFICATIONS:

• Master’s degree or PhD in Artificial Intelligence, Computer Science, or a

related field.

• 4 years of experience with SRE, InfoSec, or DevOps, with experience building

LLM evaluation (evals) pipelines, observability, and optimizing LLM-native

metrics at scale.

• 2 years of experience in vulnerability testing, security auditing,

implementing AI safety guardrails, and ensuring enterprise accuracy.

• Experience with enterprise AI data infrastructure (vector databases,

embedding generation, search architectures, data readiness, and

state-management).

• Experience designing multi-agent systems (ReAct, tool-calling), MCP, and

orchestration frameworks.

• Knowledge of GTM, sales workflows, or CRMs, with experience driving technical

adoption of zero-to-one AI products.

ABOUT THE JOB:

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.

Individual pay is determined by factors including job-related skills,

experience, and relevant education or training.

US: $186000 - $269000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google

[https://www.google.com/about/careers/applications/benefits/].

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.