Responsibilities

• Own the full LLM pipeline from data preparation to production real case usage.
• Design, iterate and optimize prompts (zero-/few-shot, chain-of-thought, tool-calling, etc.) to maximize model utility and safety across products and languages.
• Build and maintain Retrieval-Augmented Generation (RAG) QA/search systems that connect to multi-source knowledge bases.
• Familiar with vLLM/SGLang inference architectures and have proven experience deploying and operating LLM services on multi‑GPU or cluster environments.
• Design, implement and operate multi‑agent LLM architectures (e.g. LangGraph, CrewAI, AutoGen) including task decomposition, agent orchestration, memory sharing and tool‑calling workflows.
• Develop evaluation pipelines (automatic metrics & human feedback) to measure prompt and model quality, bias, and hallucination rates.
• Collaborate with product and CS teams to integrate AI models into conversational Chatbot in different scenarios.
• Track cutting-edge research, author tech blogs, and keep improve current architecture. 

Requirements

• Master’s Degree or higher in Computer Science, Data Science or related field..
• At least 2 years of deep-learning/NLP experience, including 1+ year practical LLM work (SFT, DPO, RAG, quantization, inference optimization, etc.).
• Demonstrated prompt engineering & tuning expertise (few-shot design, structured prompting, prefix-/p-tuning, reward re-ranking, safety filtering).
• Practical experience building and deploying multi‑agent LLM workflows, with understanding of agent‑orchestrator patterns, shared memory, long‑horizon planning and guard‑rail design.
• Proficient in both English and Chinese communication for efficient cross team collaboration