Senior MLOps Engineer

Klarna; Stockholms kommun; On-site; SEK 900,000 - 1,200,000; Full time

Job summary

Klarna is seeking a Senior Engineer to bridge engineering and data science, building and productionizing ML pipelines that power credit decisioning. You will move models from development to production using Python and scalable infrastructure in a cloud environment.

Your work will cover training models, deploying them into production, and collaborating with cross-functional teams to scale Klarna's credit risk and fraud capabilities for real-world use.

Qualifications

• Strong experience with Python for data science and machine learning development.
• Hands-on experience productionizing machine learning models and pipelines.
• Practical experience with tree-based models.
• Experience with AWS or comparable cloud infrastructure.
• Understanding of MLOps practices for scaling models into production.
• Experience collaborating with cross-functional teams.
• Working proficiency and communication skills in verbal and written English.

Responsibilities

• Build and productionize ML pipelines powering Klarna's credit decisioning.
• Train models and deploy them to production using scalable infrastructure.
• Collaborate with cross-functional teams to scale credit risk and fraud offerings.

Skills

Python

ML deployment

Tree-based models

AWS

MLOps

Cross-functional teamwork

English communication

Job description

As a Senior Engineer, you will work at the intersection of engineering and data science, building and productionizing machine learning pipelines that power Klarna's credit decisioning. You will take models from development through to production, ensuring pipelines are reliable, scalable, and ready for real-world use. Your day-to-day work will span writing Python to train models, building infrastructure to support the ML pipeline, and deploying models into production using tools such as tree‑based models and AWS SageMaker. You will play a key part in scaling Klarna's in‑house data science capabilities as we continue to grow our credit risk and fraud offerings.

Who you are

• Strong experience with Python for data science and machine learning development
• Hands‑on experience productionizing machine learning models and pipelines
• Practical experience with tree‑based models
• Experience with AWS or comparable cloud infrastructure
• Understanding of MLOps practices for scaling models into production
• Experience collaborating with cross‑functional teams
• Working proficiency and communication skills in verbal and written English

Awesome to have

• Experience working in credit risk, fraud, or financial services
• Experience building data pipelines at scale within a larger data science organization
• Familiarity with model monitoring and observability tools
• Experience with CI/CD tools for ML deployment