Web Solutions Engineer, Technology and Insights Partnership Systems

Google Google · Big Tech · Bengaluru, Karnataka, India

Full-stack engineer to build and deploy scalable AI applications using Google Cloud AI stack, focusing on LLMs, RAG, and agentic workflows for YouTube's business systems. The role involves integrating AI services with front-end applications, modernizing middleware, and driving data consolidation to solve complex business operations and partner management challenges.

What you'd actually do

  1. Design, build, and deploy highly scalable AI applications using the Google Cloud AI stack (Vertex AI, Cloud Functions, Agent Development Kits) to solve complex business operations and partner management challenges.
  2. Develop autonomous, multi-agent systems and robust RAG architectures that securely integrate internal knowledge bases and enterprise data silos with large language models, demonstrating an understanding of vector embeddings, context windows, and semantic search.
  3. Seamlessly integrate AI backend services (via APIs and Cloud Functions) with intuitive frontend web applications (using TypeScript/Angular/Lit) to provide insights and automated workflows to end-users.
  4. Develop project plans and deliver AI-focused projects on time, within budget, and in scope with minimal guidance. Identify opportunities where generative AI can eliminate operational friction or technical debt.
  5. Write and review complex Technical Design Documents focused on AI system architecture.

Skills

Required

  • Python
  • TypeScript
  • SQL
  • JavaScript
  • Java
  • C++
  • database technologies (e.g. SQL, NoSQL)
  • front end languages (e.g., JavaScript or TypeScript)
  • backend languages (e.g., Java, Python, or C++)

Nice to have

  • designing and optimizing databases
  • building and deploying machine learning models, LLM applications, or Generative AI solutions using public cloud infrastructure (e.g., Google Cloud Platform)
  • writing and reviewing technical documents, including design, development, and revision documents, and performing code reviews in compiled or scripted languages
  • building and scaling full-stack web applications and backend API services

What the JD emphasized

  • minimal guidance
  • complex business problems
  • ambiguous technical challenges
  • minimal guidance

Other signals

  • integrating next-generation AI (Larg Language Models (LLMs), Retrieval-augmented generation (RAG), Agentic workflows) 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
  • manage ambiguous technical challenges
  • develop project plans and scalable solutions that have an immediate, obvious impact on YouTube's global partner ecosystem