About the job You will be part of the Artificial Intelligence Engineering team, in the Group Emerging Technologies and Data (GETD) division of AXA.
This transversal team's mission is both to build AI-powered initiatives (proofs of concept, proofs of value, pilots) with AXA entities & strategic partners and to define & implement AI best practices, tools, and collaboration models to be followed across the whole AXA Group.
Our team is composed of 10 people, spread in 3 countries (France, Spain & Switzerland) and we work in hybrid mode (60% remote 40% on-site).
AXA is a global leader in insurance and asset management present in nearly 60 countries.
We leverage Artificial Intelligence to protect our 100 million customers, in every domain of core insurance (Property & Casualty, Life & Savings, Health, ..
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).
As a responsible company, AXA defined and follows strong Responsible AI principles around robustness, interpretability, fairness, and sustainability.
Key responsibilities In this role, you will:
Enhance end-to-end machine learning solutions developed and deployed on the Databricks platform, from data pre-processing and feature engineering to model training, evaluation, deployment and monitoring.
Enhance and implement Databricks templates/recipes to boost the scalability of AI use cases.
Refactor notebooks into high quality code scripts that will be part of the ML pipelines.
Design and optimize ML pipelines on Databricks, leveraging its features and capabilities to streamline the model development and deployment process.
Build and maintain scalable and reliable MLOps infrastructure on Databricks, facilitating model versioning, monitoring, and automated deployment.
Codify best practices for model performance monitoring, model drift detection, and model retraining, ensuring high-quality and up-to-date ML models in production.
Implement repeatable and reliable CI/CD pipelines.
Implement data, model and code tests.
Stay up to date with the latest advancements in MLOps, LLMOps and Databricks technologies, and proactively identify opportunities to enhance our ML capabilities.
Mentor and provide technical guidance to the Data Science teams across AXA entities.
Build assets and guidelines with our internal MLOps community.
Participate in data governance initiatives to ensure the compliance and integrity of the AI Products.
What is needed to succeed? As we want you to succeed in this role, here is a list of examples of key factors:
5 years of hands-on experience as a ML Engineer in pushing AI Products in production based on MLOps best practices.
(no notebooks in production) Strong proficiency in Python.
Familiar with cookiecutter and Makefile.
A good understanding of Agile methodologies and a mindset of continuous improvement.
Ability to articulate the results of your work for operational teams, middle and top management.
Excellent communication in English and interpersonal skills for working in a multicultural work environment.
Passion about solving challenging problems leveraging new technologies.
Nice to have Here are other elements we will consider:
Practical knowledge of Infrastructure as Code Practical knowledge of data and model monitoring to detect data, concept and target drifts.
