液化空气集团早在1916年就进入中国，70年代开始向中国提供空分设备，经过多年的稳步发展，目前在中国设有近120家工厂，遍布40多个城市，拥有约5000名员工。集团在华主要经营范围包括工业及医用气体的运营，家庭健康服务，工程与制造业务，以及全球市场与技术和上海创新园从事的创新业务。公司业务已覆盖中国主要的沿海工业区域，并继续向中部、南部和西部地区拓展。
液化空气通过创造卓越绩效和履行责任追求盈利性增长和长期可持续发展，并保持在中国的行业领先地位。依托于集团的长期战略与全球资源，公司聚焦能源、环境、高科技和健康等领域，以迎接挑战并创造新的市场机遇。凭借专业团队的全力支持，公司致力于为客户提供可信赖的服务与高附加值解决方案，同时履行企业社会责任。
The objective of this position is to drive innovation in projects focused on the development and application of data analytics tools to support our engineering (e.g., ASU engineering) and operations (e.g., ASU, SMR, Electronics Carry Gas).
Collaborate with business entities to define the problem and business requirements, translate them into functional design specifications, and develop solutions. Identify, evaluate and select industrial or academic partners as needed.
Lead and participate in the design, development, and deployment of AI solutions based on Large Language Models (LLMs) to address key challenges in industrial, R&D, and healthcare domains.
Lead the fine-tuning and optimization of open-source LLMs (e.g., Llama, Qwen, DeepSeek) for specific business scenarios (such as technical document comprehension, process parameter optimization, safety report analysis, and scientific knowledge mining).
Expertly apply Retrieval-Augmented Generation (RAG) techniques, integrating internal knowledge bases (e.g., technical patents, engineering manuals, research reports) with external data to build high-accuracy intelligent Q&A, content generation, and knowledge management systems.
Test and verify the performance of solutions with prototypes developed.
Define and develop business tools based upon the prototype performance verification, ensure transfer of the tool to the operational entities and provide support for the industrial deployment.
Train team members on the details of the implemented methodology, thus ensuring sustainability of the solution for Air Liquide.
Support knowledge transfer within Air Liquide. Publish research in internal R&D reports, at conferences and potentially in peer-reviewed journals.
Work with IT, internal, and external organizations to obtain, clean, visualize, and analyze data.
Continuously track the latest advancements in NLP, LLM, and Generative AI (GenAI) (e.g., Agents, Multi-modality), evaluating and introducing new technologies to enhance team capabilities.
M.S. or Ph.D. in Computer Science, Artificial Intelligence, Statistics, Mathematics, Engineering or related fields. Independent and inter-disciplinary research experience are preferred.
Solid, practical experience in LLM fine-tuning with a deep understanding of its principles.
In-depth understanding of RAG architecture with at least one complete, deployed RAG project. Familiarity with relevant frameworks.
Excellent fundamental understanding of statistics (e.g. distributions, probability, linear regressions) is a must. Knowledge of advanced statistics (e.g. clustering, elastic net, MLE, dimension reduction (PCA, PLS, etc), stochastic process, bayesian network, time series models) and machine learning models (e.g. decision trees, random forest, SVM) are of benefit.
Programming experience with R and Python are preferred. Knowledge of Java, C++, or Javascript is also of benefit.
Excellent communication and interpersonal skills (written and oral). Must be comfortable to work in English on a daily basis and in a multi-disciplinary and international team. Knowledge of French is of benefit.
