Description

Contexte

The mission will be part of the Artificial Intelligence Engineering project, in the GDAI division of AXA. In the AI Engineering project, we help entities scale and define and apply best practices around ML / LLM Engineering. We are delivering an ML/LLM Engineering learning path across AXA globally and need someone to support this delivery.

Missions

• Enhance MLOps/ ML Engineering Training existing repository used across AXA globally
• Evolution to ML pipeline track on AWS and Databricks (currently using Azure)
• Facilitate workshops that we give to entities on the training
• Bring expertise around the LLM Engineering best practices
• Support the content creation for LLM Engineering practices (that might be complex). There is a work around translating complex practices into simple explanation
• Implement these practices in a sandbox environment and benchmark tooling
• (Optionnal) Support the project in optimizing POC/MVP in entities around LLMOps practices

Profil recherché

Expertise: - Confirmed expertise in ML/LLM Engineering and Data Science
- Expertise with the ability to simplify complex best practices
- Expertise on ML Engineering and LLM Engineering best practices
- Ideally seen LLM based model/application in production and understand the fix that might be done
Technical Expertise: - Azure and AWS (secondary) technological stack expertise for ML/LLM/AI
- CI/CD expertise (GitHub actions/Azure DevOps preferred)
- Expertise in deploying ML/LLM pipeline in a Cloud based environment
-Communicate effectively
-Adapt in a complex environment

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