Web Solutions Engineer, Technology and Insights Partnership Systems Youtube

Google Google · Big Tech · Bengaluru, Karnataka, India

This role involves designing, building, and deploying scalable web applications and back-end services for YouTube's content partner ecosystem. A key responsibility is integrating AI/ML technologies into business platforms to enhance operational capacity and provide conversational analytics. The role requires modernizing legacy systems, managing data pipelines, and collaborating with stakeholders to translate business needs into engineering solutions. Experience with full-stack development, system design, and AI/ML technologies like LLMs, vector embeddings, RAG, and agentic AI is preferred.

What you'd actually do

  1. Design, build, and deploy highly scalable web applications, back-end services, and data platforms that manage YouTube’s content partner ecosystem, demonstrating a solid understanding of algorithms, data structures, and scaling factors.
  2. Drive the integration of industry-leading AI/ML technologies into core business platforms to expand operational capacity and provide conversational analytics to end-users.
  3. Execute complex system migrations and help re-architect legacy back-end systems into modular, highly available services with API endpoints.
  4. Ensure data pipelines and system reliability. Implement comprehensive testing, monitoring, and alerting while migrating workflows to modern cloud data warehouses.
  5. Write and review complex Technical Design Documents (TDDs) and perform thorough code reviews. Collaborate across time zones with stakeholders, escalating concerns appropriately and acting as a technical consultant to translate business objectives into tangible engineering solutions.

Skills

Required

  • Bachelor's degree in Computer Science, Electrical Engineering, Math or related quantitative field, or equivalent practical experience in software development.
  • 4 years of experience in full-stack software development and system design.
  • Experience with front-end languages (e.g., JavaScript or TypeScript).
  • Experience with back-end languages (e.g., Java, Python, or C++).
  • Experience working with database technologies (e.g., SQL, NoSQL).

Nice to have

  • Experience writing and reviewing technical documents and performing code reviews in compiled or scripted languages.
  • Experience implementing software unit testing, integration testing, and system monitoring.
  • Experience building and scaling full-stack web applications and back-end systems and developing with generative AI, large language models, vector embeddings, RAG, or agentic AI.
  • Experience with protocol buffers, gRPC, and distributed data processing frameworks and experience in system refactoring, technical debt reduction, and migrating legacy data silos to modern, distributed architectures.
  • Experience working with varied monitoring and testing solutions.
  • Proficient in multiple programming languages and libraries including Python, Java, Angular, Lit, and Redux.

What the JD emphasized

  • integrating next-generation AI into daily operations
  • modernizing legacy enterprise middleware
  • driving data consolidation
  • bridge the gap between intuitive front-end user experiences and highly complex back-end data pipelines
  • address ambiguous technical challenges
  • manage edge cases effectively
  • collaborate closely with technical stakeholders
  • develop project plans and scalable solutions
  • impact on YouTube's global partner ecosystem
  • industry-leading AI/ML technologies
  • conversational analytics
  • complex system migrations
  • re-architect legacy back-end systems
  • modular, highly available services
  • API endpoints
  • data pipelines and system reliability
  • comprehensive testing, monitoring, and alerting
  • modern cloud data warehouses
  • complex Technical Design Documents (TDDs)
  • thorough code reviews
  • translate business objectives into tangible engineering solutions

Other signals

  • integrating next-generation AI into daily operations
  • modernizing legacy enterprise middleware
  • driving data consolidation
  • bridge the gap between intuitive front-end user experiences and highly complex back-end data pipelines
  • address ambiguous technical challenges
  • manage edge cases effectively
  • collaborate closely with technical stakeholders
  • develop project plans and scalable solutions
  • impact on YouTube's global partner ecosystem