ROLE OVERVIEW This role will be the technical foundation builder for the company’s AI transformation. You will design and build the company-wide knowledge infrastructure and context layer that powers future AI applications. This is a highly hands-on role requiring strong backend engineering capability, LLM application experience, product sense, and the ability to operate independently in a fast-moving, ambiguous environment. KEY RESPONSIBILITIES * Design and build the company-wide AI knowledge infrastructure, including company wiki, internal knowledge base, retrieval layer, and context management system. * Develop scalable LLM application architecture, including RAG pipelines, vector database integration, prompt workflows, API services, monitoring, and deployment. * Own the end-to-end technical delivery of internal AI tools, from backend architecture and basic frontend integration to deployment, testing, and monitoring. * Work closely with business, brand, PR, IR, and leadership stakeholders to translate ambiguous business needs into practical AI systems and technical roadmaps. * Optimize system performance, including token efficiency, latency, caching strategy, retrieval quality, data architecture, and model inference flow. * Evaluate and integrate AI coding tools, LLM frameworks, vector databases, and third-party APIs to improve development efficiency and product quality. * Mentor junior engineers or interns when needed, and help establish technical standards, documentation practices, and reusable engineering workflows. REQUIREMENTS * 4–7 years of backend engineering experience, with at least 2 years of hands-on LLM application development experience. * Strong backend development skills in Python; experience with Node.js or Go is a plus. * Solid computer science fundamentals, including algorithms, system design, database design, API architecture, distributed systems, caching, and performance optimization. * Production-level LLM application experience, not limited to demos or prototypes. Experience should include prompt engineering at scale, model selection, inference pipeline design, or RAG architecture. * Hands-on experience with RAG and vector databases such as Pinecone, Weaviate, Chroma, or similar tools. * Experience owning full engineering delivery, including backend services, basic frontend integration, API deployment, monitoring, and troubleshooting. * Heavy user of AI coding tools such as Cursor, Claude Code, GitHub Copilot, or similar tools. * Mandarin fluency is required; English working proficiency is required. * Able to work independently under ambiguous instructions and make sound technical decisions without waiting for detailed specifications.