Machine Learning Engineer > AXA Group Operations > Joboolo FR :
Société : AXA Group Operations Lieu : Paris
Ready to shape the future? Join our team as a MachineLearningEngineer Extraordinaire About the job Based in Paris or Barcelona, 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 MLOps 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, ..
.
).
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:
Build and improve reusable tools & modelling pipelines and support knowledge sharing across several teams.
Work with Data Scientists to improve both technical and statistical performance of models.
Convert the machinelearning models into application program interfaces (APIs) so that other applications can use them in alignment with architecture & infrastructure standards.
Secure and monitor ML processing, including safeguards, A/B testing, fault-tolerance, and failover.
Contribute to the definition and deployment of best practices in MachineLearning & MLOps, Contribute to the sharing of knowledge and expertise through communities and working groups (internal and external).
Help the different actors of the organization (such as product managers and stakeholders) understand what results they gain from MLOps and best engineering practices in Data and AI.
What is needed to succeed As we want you to succeed in this role, here is a list of examples of key factors:
Proficiency in ML Ops and ML Engineering frameworks:
experiment trackers (like mlFlow) & orchestrators (Airflow, Kubeflow, Sagemaker Pipeline) A practical knowledge in one of the popular ML Python libraries (TensorFlow, PyTorch, Keras, Scikit-Learn) and Open-Source libraries.
A good understanding of Agile methodologies and a mindset of continuous improvement.
Ability to articulate the results of your work for various audiences.
Good 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:
2 years of experience in delivering and running ML models in production, using at least one of some of the main Big Data frameworks and platforms:
Spark, Databricks, Snowflake, ..
.
Practical knowledge in Infrastructure as code (Terraform, CloudFormation, ..
.
).
Practical knowledge of cloud services (Azure or Amazon Web Services).
Theoretical knowledge in Event Driven Architecture (using Kafka, Event Hub, or Rabbit MQ).
Insurance & Finance functional knowledge What we offer On top of usual benefits, we also offer:
Hybrid working (60% remote 40% on-site).
Global communities of practice and 2 yearly global events gathering Engineers and Data Scientists.
Learning and mentoring opportunities through partnerships with LinkedIn Learning and O'Reilly.
Among a strong Employee benefit program, mental health, and well-being platform to access personalised care.
We bring together the expertise, cultural diversity and creativity of over 8,000 employees worldwide.
We're committed to equal opportunities in all aspects of employment (gender, LGBT, disabled persons, or people of different origins) and to promoting Diversity & Inclusion by creating a work environment where all employees are treated with dignity and respect, and where individual differences are valued.
