ML Engineer Expert
il y a 4 semaines
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.
- Challenge Data Scientists to improve both technical and statistical performance of models.
- Convert the machine learning 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.
- Define and deploy best practices in Machine Learning & MLOps, mentor and teach colleagues across the group.
- Actively 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:
- 5+ years of experience with DevOps: versioning (Git), containers (Docker/Kubernetes), CI/CD, Static analysis tools, …
- 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.
- 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:
- 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.
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