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 Data Science R&D team at Air Liquide has an opening for a full time researcher focused on engineering and operation data analysis.
The researcher will have the opportunity to work on challenging problems to support Air Liquide’s customer-centric transformation through projects supporting our engineering (e.g., ASU engineering) and operations (e.g., ASU, SMR, Electrolyzer).
The researcher will develop and maintain a strong interaction with Air Liquide’s business groups at Air Liquide's state-of-the-art innovation campus located in the Minhang District of Shanghai as well as other nearby Air Liquide sites. The Innovation Campus Shanghai hosts operational, marketing, business development, and advanced technology teams on-site with our R&D teams.
Please follow the link below to know more about the company and the OR & Analytics work that we do in our group: http://analytics-magazine.org/company-profile-air-liquide/

您将如何贡献和成长？

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). Focusing on the business needs of Air Liquide in the context of our customer-centric transformation, the researcher will be responsible to: 

• 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.

您是合适的人选么？

Request Profiile:

• 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.

PREFERRED：

• Project experience in industrial manufacturing (e.g., chemical, energy), semiconductors, healthcare, or supply chain is preferred.

• Familiarity with the selection, deployment, and optimization of vector databases (e.g., Milvus, Pinecone, Chroma).

• Familiarity with AI services and tools on at least one cloud platform (AWS, Azure).

• Experience with AIGC, multi-modal models, or AI Agent development.

• MLOps experience (model deployment and serving), familiar with tools like Docker, Kubernetes, FastAPI/Gradio.

• Self motivated individual with ability to define and solve problems in collaborative ways across teams from different backgrounds.

• Publications in top-tier AI/NLP conferences or journals are a plus.

关于液化空气集团

液化空气集团 —— 全球工业与健康领域气体、技术和服务的领导者，业务遍及78个国家/地区，员工约64,500
人，为380多万名客户与患者提供服务。氧气、氮气和氢气是生命、物质及能源不可或缺的小分子。它们象征着液化空气的科学疆域，自集团1902年成立以来，始终位于其业务的核心。

多样化造就了我们的业绩

在液化空气，我们致力于建立一个多元化和包容性的工作场所，拥抱来自全球的员工、客户、患者、社区利益相关者和文化的多样性。
我们欢迎并考虑所有合格申请人的申请，无论他们的背景如何。我们坚信，多元化的组织为人们提供了展示个人和集体才能的机会，它有助于培养我们的创新能力，通过践行我们的基本原则、为我们的成功而努力并在不断变化的世界中创造一个有吸引力的环境。