AI Engineer - LLM-Based Multi-Agent Systems for B2B Optimization Sorbonne University

Job Description

Join a cutting-edge research project at the Sorbonne Center for Artificial Intelligence Laboratory in Abu Dhabi, United Arab Emirates. At the forefront of Artificial Intelligence, this project combines the expertise of Sorbonne Université Center for Artificial Intelligence, PostGenAI@Paris cluster and APTIVIO's AI-powered sales optimization platform. We are seeking a highly motivated and experienced AI Engineer to contribute to the development and implementation of Large Language Model (LLM)-based agents within multi-agent systems for B2B decision optimization. This project aims to explore how LLM-based agents, embedded in a multi-agent environment, can collaboratively address complex challenges in B2B markets. You will be part of a team developing and refining these LLM-based agents, contributing to the design, implementation, and evaluation of their performance within a real-world context.

In this role, you will:

Be part of a cutting-edge research project at the intersection of AI, multi-agent systems, and B2B optimization.

Collaborate with leading researchers and industry experts in a stimulating and intellectually challenging environment.

Contribute to developing innovative solutions that address real-world business challenges.

Access state-of-the-art infrastructure and resources.

The 2 years position is based in the United Arab Emirates, at the Abu Dhabi campus of the Sorbonne Center for Artificial Intelligence, in the frame of an industrial partnership with Aptivio.

Qualification

Minimum Qualifications:

Master's degree or PhD in Computer Science, Data Science, Artificial Intelligence, or a related field.

Proven experience with LLM frameworks (e.g., ollama, langchain) and fine-tuning techniques.

Minimum Experience:

3 years of experience as a Lab engineer, including the PhD.

Job-Specific Skills:

Strong understanding of machine learning principles, particularly deep learning architectures (e.g., transformers, RNNs).

Familiarity with multi-agent systems and agent-based modeling.

Proficiency in Python and relevant data science libraries (e.g., PyTorch, Scikit-learn).

Excellent analytical and problem-solving skills.

Strong communication and teamwork abilities.

Experience in a research environment is highly desirable.

Good practical skills, and the ability to devise, set up and run tests in the lab.

Curiosity, creativity, and strong interest for scientific research.

Languages: English is mandatory; French and Arabic are added advantage.