+ Applied Machine Learning Engineer – 18194

Veritaz is a leading IT staffing solutions provider in Sweden, committed to advancing individual careers and aiding employers in ensuring the perfect talent fit. With a proven track record of successful partnerships with top companies, we have rapidly grown our presence in the USA, Europe, and Sweden as a dependable and trusted resource within the IT industry.

Assignment Description

We are seeking a Machine Learning Engineer

What You Will Work On

• Develop, train, and optimize machine learning models for production use cases
• Build and maintain end-to-end ML pipelines and workflows
• Perform feature engineering and exploratory data analysis (EDA)
• Deploy machine learning models into production environments
• Implement model retraining, calibration, and continuous improvement strategies
• Develop predictive models and decision-support systems
• Work with model-driven decisioning and optimization approaches
• Explore and implement bandit algorithms for dynamic decision-making
• Monitor model performance and ensure stability in production systems
• Improve existing ML workflows for scalability and robustness
• Collaborate with data engineers, software engineers, and business stakeholders
• Contribute to development of new ML-powered features and capabilities
• Support automation of intelligent decision-making systems
• Ensure reproducibility, reliability, and observability of ML systems

What You Bring

• Experience in Machine Learning Engineering or Applied Machine Learning
• Strong understanding of predictive modeling techniques
• Experience with model retraining and lifecycle management
• Hands-on experience with production ML systems
• Experience in exploratory data analysis (EDA) and feature engineering
• Experience with model-driven decisioning systems
• Knowledge of TensorFlow or similar ML frameworks
• Experience deploying machine learning models into production
• Understanding of scalable ML system design and architecture
• Ability to collaborate effectively with cross-functional teams