Cloud Data Engineer for AI Pipelines

Verisure; Malmö kommun; On-site; SEK 900,000 - 1,200,000; Full time

Job summary

Verisure in Malmö is seeking a Data Engineer for the AI Models & Alarm Intelligence team to build data infrastructure powering AI products across countries.

You will implement cloud-based data platforms, maintain the data lake, and collaborate with ML engineers and developers to enable training, evaluation, deployment and monitoring of AI models. The role blends hands-on engineering with architectural thinking.

Qualifications

• Strong Python skills and production-grade data pipelines.
• Experience with AWS and cloud-native architecture.
• Experience building and maintaining S3-based data platforms.
• Experience with Docker containers.
• Understanding APIs, cloud services, and distributed systems.
• Experience collaborating with software engineers and stakeholders.

Responsibilities

• Build and maintain data pipelines supporting AI model training, evaluation, and deployment.
• Collaborate with AI Ops teams, developers, and stakeholders across multiple countries to enable scalable AI infrastructure.
• Develop and improve cloud-based solutions for collecting, processing, and storing data from connected devices.
• Maintain and evolve our data lake, training infrastructure, monitoring capabilities, and feedback loops from products in the field.
• Collaborate with engineers across R&D to reduce complexity, improve reliability, and support future AI initiatives.
• Help shape the long-term data architecture that will support the next generation of AI-powered products.

Skills

Python

AWS

Data pipelines

Docker

Cloud architecture

APIs

Collaboration

Tools

S3

Snowflake

SageMaker

Docker

Kubernetes

Job description

Verisure in Malmö is seeking a Data Engineer for the AI Models & Alarm Intelligence team to build data infrastructure powering AI products across countries.

You will implement cloud-based data platforms, maintain the data lake, and collaborate with ML engineers and developers to enable training, evaluation, deployment and monitoring of AI models. The role blends hands-on engineering with architectural thinking.