ML Platform Engineer: GenAI & MLOps (Hybrid)

AXA IT Solutions; Warszawa; Hybrid; PLN 180,000 - 240,000; Full time

Benefits offered by this job

Hybrid work model

Job summary

AXA is launching an MLOps initiative to modernize actuarial pricing model development and deployment. You will work with ML Engineering and actuaries to ensure reproducibility, monitoring, and scalable pipelines in a hybrid, international environment.

The role involves building and operating an AI platform for secure document processing and GenAI-enabled workflows, with emphasis on reliability, cost control, and data governance.

Qualifications

• Bachelor's or Master's degree in Mathematics, Computer Science, ML, or related field.
• Strong experience with Python data science stacks and MLOps tools.
• Experience building GenAI agentic workflows and dashboarding basics.

Responsibilities

• Create CI templates for model development with version control, testing, and reproducibility.
• Collaborate with ML Engineering and actuaries to audit and optimize pipelines.
• Develop monitoring strategies for performance, reliability, and efficiency.
• Oversee end-to-end operation of the AI platform, ensuring high availability and secure data handling.
• Manage cloud resources to optimize cost, performance, and security compliance.

Skills

Pandas

PySpark

scikit-learn

SHAP

MLFlow

Kedro/Airflow

Great Expectations

Langchain

smolagents

Power BI

Tableau

CI/CD

GitHub Actions

FastAPI

Flask

AWS

Azure

GCP

Docker

Kubernetes

Education

Bachelor's or Master's in Mathematics, Computer Science, ML

Tools

Docker

Kubernetes

Flask

FastAPI

GitHub Actions

MLFlow

Langchain

Airflow

PySpark

Job description

AXA is launching an MLOps initiative to modernize actuarial pricing model development and deployment. You will work with ML Engineering and actuaries to ensure reproducibility, monitoring, and scalable pipelines in a hybrid, international environment.

The role involves building and operating an AI platform for secure document processing and GenAI-enabled workflows, with emphasis on reliability, cost control, and data governance.