Technical Environnement

The technical environnements are diversified. Our main daily tools are:

• Strong proficiency in Python (mastering another language would be greatly appreciated),
• Expertise in ML techniques, LLM ecosystem, and open-source techniques.
• Experience with key tools and libraries: LangGraph, LlamaIndex, RAG, VectorDB, model quantization, and deployment frameworks (vLLM, …).
• Familiarity with one or more cloud platforms (AWS, GCP).
• Understanding MLOps workflows, CI/CD, and version control systems like Git- 1 year minimum of professional experience after your studies and internship;
• Master, engineering degree or PhD (STEM);
• Strong value-oriented mindset and intellectual curiosity to understand our customers needs;
• Hands-on experience with Retrieval-Augmented Generation (RAG) systems and LLM systems;
• Skills in software development;
• Excellent spoken and written communication skills in French and English, to convey the values of your work to various stakeholders.

What you will learn

• Buisness and AI challenges of changing sectors with the major stakeholders;
• Development in diversifed technical environments, always with a strong “data” focus, from the prototype to the production phases;
• Knowledge of good practices in terms of development and production deployment, supported by Senior Data Scientists and the Tech-Dev Team;
• Keys to apply Machine Learning algorithms on Buisness cases;
• You will have some time dedicated to training and differents options to develop your skills: MOOC, Peer coding, Data Science conferences …
• And you will contribute to the development of an ambitious growing start-up!

The estimated salary package for this role ranges from 45 000€ to 50 000€ gross annual, plus an annual variable.The recruitment process is made of 3 steps:

1. An interview (45 min), during which you will present yourself and your interests, along with a presentation of Craft.AI and our missions;
2. A technical interview with a Data Scientist on Machine Learning basics, some live coding and how to treat a real-world usecase;
3. A small usecase to deal with on your side with a last encounter to meet the Team and present what you did.

We particularly encourage individuals with under-represented profiles on the local job market to apply to this opportunity.