Tech Stack
ApacheAWSCloudKubernetesPySparkPythonPyTorchSparkTensorflow
About the role
- Dive into AI projects to build and optimize machine learning models for production use cases, ensuring efficiency and scalability
- Design, develop, and deploy scalable machine learning products
- Design and build ML infrastructure and tooling, often in cloud environments, to support large-scale deployments
- Build and maintain infrastructure and tooling as a platform for data scientists
- Collaborate with cross-functional teams to integrate ML solutions into production systems and with clients
- Monitor and maintain the performance and reliability of ML models post-deployment
- Stay current with the latest advancements in machine learning and related technologies
- Support colleagues and contribute to knowledge sharing
Requirements
- At least 3-4 years of experience as a Machine Learning Engineer with a proven track-record of delivering ML models in production
- Cloud experience (e.g., AWS, Google Cloud, Kubernetes)
- Experience with distributed data tools (e.g., Spark, Dask, PySpark, Apache Beam)
- Expertise in Python and modern ML/AI libraries (PyTorch/TensorFlow, Transformers, LangChain, LangGraph, Vertex AI)
- Strong understanding of ML infrastructure: deployment, monitoring, debugging, optimization
- Degree in Computer Science, Engineering, Mathematics, Statistics, or related field
- Based in Sweden — must have Swedish citizenship, a valid work visa, or permanent residency
- Fluent in English (spoken and written)