Lead ML Engineer: Credit Modeling & Production Pipelines

Klarna; Warszawa; Hybrid; PLN 250,000 - 420,000; Full time

Job summary

Klarna is seeking a Lead Engineer for the Credit Modeling Pipeline in Warsaw/Stockholm setup. You will take consumer credit underwriting models from a data scientist's experiment to reliable production systems, writing production Python and building the ML pipeline infra.

You will deploy models with AWS SageMaker, monitor performance, and troubleshoot end-to-end, collaborating closely with data scientists to scale Klarna's in-house data science capability as the credit risk and fraud teams grow.

Qualifications

• Production experience with Python for machine learning, not just notebooks.
• Experience taking ML pipelines from development to production and owning them.
• Hands-on with tree-based models.
• Experience deploying ML workloads on AWS or similar cloud platforms.
• Understanding full SDLC and applying it to ML code.

Responsibilities

• Write production Python to train and deploy credit underwriting models.
• Build and maintain ML pipeline infrastructure from feature computation to retraining and monitoring.
• Deploy models in production using AWS SageMaker and keep them running.
• Troubleshoot end-to-end pipeline issues.
• Collaborate with data scientists to scale Klarna's in-house data science capability.

Skills

Production Python for ML

ML model deployment

ML pipelines

AWS SageMaker

Tree-based models

CI/CD for ML

Cross-functional collaboration

Fluent English

Tools

Python

AWS SageMaker

Git

CI/CD pipelines

Job description

Klarna is seeking a Lead Engineer for the Credit Modeling Pipeline in Warsaw/Stockholm setup. You will take consumer credit underwriting models from a data scientist's experiment to reliable production systems, writing production Python and building the ML pipeline infra.

You will deploy models with AWS SageMaker, monitor performance, and troubleshoot end-to-end, collaborating closely with data scientists to scale Klarna's in-house data science capability as the credit risk and fraud teams grow.