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

• Shape the future with the world’s leading blockchain ecosystem
• Collaborate with world-class talent in a user-centric global organization with a flat structure
• Tackle unique, fast-paced projects with autonomy in an innovative environment
• Thrive in a results-driven workplace with opportunities for career growth and continuous learning
• Competitive salary and company benefits
• Work-from-home arrangement (the arrangement may vary depending on the work nature of the business team)

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.