Senior AI Engineer

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.