<div>Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users. We are trusted by over 250 million people in 100+ countries for our industry-leading security, user fund transparency, trading engine speed, deep liquidity, and an unmatched portfolio of digital-asset products. Binance offerings range from trading and finance to education, research, payments, institutional services, Web3 features, and more. We leverage the power of digital assets and blockchain to build an inclusive financial ecosystem to advance the freedom of money and improve financial access for people around the world.</div><div><br /></div><div>We are seeking a highly skilled professional to join our team, focusing on advancing customer service scheduling optimization through innovative AI solutions. This role involves researching and implementing cutting-edge algorithms to enhance scheduling systems, leveraging business domain knowledge to elevate the impact of AI products. The successful candidate will develop and refine Large Language Models (LLMs) to extract actionable insights, improve business decision-making, and optimize prompt design for more accurate outputs. Additionally, the role includes creating scalable and robust LLM/RAG frameworks tailored to customer service scheduling, fostering innovation and maintaining a competitive market edge.</div><h3>Responsibilities:</h3><ul><li>Own the <b>full LLM pipeline</b> from data preparation to production real case usage. </li><li>Design, iterate and <b>optimize prompts</b> (zero-/few-shot, chain-of-thought, tool-calling, etc.) to maximize model utility and safety across products and languages.</li><li>Build and maintain <b>Retrieval-Augmented Generation (RAG)</b> QA/search systems that connect to multi-source knowledge bases.</li><li>Familiar with vLLM/SGLang inference architectures and have proven experience deploying and operating LLM services on multi‑GPU or cluster environments.</li><li>Design, implement and operate multi‑agent LLM architectures (e.g. LangGraph, CrewAI, AutoGen) including task decomposition, agent orchestration, memory sharing and tool‑calling workflows. </li><li>Develop <b>evaluation pipelines</b> (automatic metrics &amp; human feedback) to measure prompt and model quality, bias, and hallucination rates.</li><li>Collaborate with product and CS teams to integrate AI models into conversational Chatbot in different scenarios.</li><li>Track cutting-edge research, author tech blogs, and keep improve current architecture. </li></ul><h3>Qualifications:</h3><ul><li>Master’s degree or higher in Computer Science, Data Science or related field..</li><li><b>2+ years</b> of deep-learning/NLP experience, including <b>1+ year practical LLM work</b> (SFT, DPO, RAG, quantization, inference optimization, etc.).</li><li>Demonstrated <b>prompt engineering &amp; tuning</b> expertise (few-shot design, structured prompting, prefix-/p-tuning, reward re-ranking, safety filtering).</li><li>Practical experience building and deploying multi‑agent LLM workflows, with understanding of agent‑orchestrator patterns, shared memory, long‑horizon planning and guard‑rail design.</li><li>Clean coding practices, good English communication skills, and a passion for rapid learning.</li><li>Excellent self-driven and ownership with good deliverables.</li><li>Eager to learn, be curious about AI new technologies </li><li>Good communication and collaboration skills</li></ul>