Machine Learning Engineer

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

We are an internal software house operating within the international insurance group AXA. We provide IT solutions for the needs of AXA companies in Europe. We work in English on a daily basis, in close-knit teams, carrying out international development projects.

About the project:

We are launching an MLOps initiative to modernize the development of our actuarial pricing models by integrating best practices in machine learning operations. This project will involve automating model training, deployment, and monitoring processes, ensuring that our actuaries can operate with increased efficiency, reproducibility, and scalability in a production environment.

We are developing an AI-powered platform that leverages GenAI to accurately extract, analyze, and categorize information from large volumes of documents. This platform aims to streamline document processing workflows and enhance the speed and precision of data retrieval across various internal use cases.

Your responsibilities:

• Create continuous integration templates tailored for model development ensuring version control, testing, and reproducibility of our actuarial pricing models and datasets.
• Close work with members of the ML Engineering team and actuaries to audit and optimize the reliability and scalability of the actuaries' model training pipelines.
• Develop effective monitoring strategies to track the performance, reliability, and efficiency of the system.
• Manage the end-to-end operation of the AI platform to guarantee high availability, responsive performance, and secure data handling during document ingestion and processing.
• Oversee the integration and management of cloud resources to optimize cost, performance, and compliance with security standards, thereby enabling continuous innovation on the platform.

Our requirements:

• Bachelor's or Master's degree in Mathematics, Computer Science, Machine Learning, or related field.
• Mastery over Data Science frameworks (pandas, pyspark, sklearn and shap) and MLOPS frameworks (MLFlow, Kedro/Airflow, Hyperopt/Optuna and Great Expectations) in Python.
• Experience with building GenAI agentic workflows using Langchain or smolagents.
• Basic familiarity with Dashboarding tools (PowerBI/Tableau).
• Strong understanding of DevOps methodologies (CI/CD) and experience implementing Github Actions (or similar) workflows.
• Experience with serving models with APIs using Flask or FastAPI.
• Experience with cloud platforms (e.g., AWS, Azure, GCP) and containerization (e.g., Docker, Kubernetes).
• Extremely high attention to detail and rigor.

What we offer:

• The opportunity to influence technological solutions and product direction in an international financial organization
• Ambitious projects with a high degree of autonomy and responsibility
• A stable, long-term assignment with flexible working hours and a hybrid work model