Minimum qualifications:
- Bachelor's degree in Computer Science, Electrical Engineering, Math or a related quantitative field, or equivalent practical experience.
- 6 years of experience in full-stack software development and system design.
- Experience with back-end langu,ges (e.g., Java, Python, or C++).
- Experience with front-end languages (e.g., JavaScript or TypeScript).
- Experience working with database technologies (e.g., SQL, NoSQL).
- Experience building AI-powered products, featuring technologies such as long context windows, Retrieval Augmented Generation (RAG), etc.
Preferred qualifications:
- 2 years of experience as a tech lead or engineering manager, including taking ownership of zero-to-one product delivery.
- 2 years of experience with Site Reliability Engineering, Information Security, or DevOps practices, paired with practical experience in enterprise AI solutions.
- Experience building advanced GenAI architectures, including multi-step Large Language Model (LLM) applications, multi-agent systems, or integrating the MCP with modern orchestration frameworks.
- Experience with AI data infrastructure, including vector databases, embedding generation, search architectures, and state-management challenges for AI systems.
- Experience with Go-To-Market operations and enterprise sales workflows, or experience building tools that integrate with enterprise business systems.
About the job
At YouTube, we believe that everyone deserves to have a voice, and that the world is a better place when we listen, share, and build community through our stories. We work together to give everyone the power to share their story, explore what they love, and connect with one another in the process. Working at the intersection of cutting-edge technology and boundless creativity, we move at the speed of culture with a shared goal to show people the world. We explore new ideas, solve real problems, and have fun — and we do it all together.Responsibilities
- Lead the delivery of complex AI applications, transitioning from rapid prototypes to production-grade, AI-powered solutions (e.g., multi-agent systems, Model Context Protocol (MCP) servers) that drive measurable return on investment.
- Prototype rapid proof-of-concepts (PoCs) using frontier GenAI (e.g., LLMs, RAG, agentic frameworks) to demonstrate technical feasibility to stakeholders, translating ambiguous business problems into tangible software solutions.
- Ship enterprise-grade solutions by owning the full software development lifecycle, authoring technical design documents, writing clean code, building front-end, and executing delivery.
- Engineer high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet rigorous requirements for accuracy, safety, and latency.
- Solve enterprise AI blockers by systematically identifying and resolving technical issues including data readiness gaps, system integration complexities, and agentic state-management challenges.
