MACHINE LEARNING ENGINEER

About this job

Company description: Scandinavian Airlines System SAS Job description: Challenges you will work on At SAS, machine learning has moved well beyond experimentation. As part of our ongoing Digital & IT transformation, we are expanding our investments in machine learning, automation, data platforms, and Generative AI. We design, deploy, and operate production models that support pricing decisions, personalize customer experiences, and make our operations smarter every day. As a Machine Learning Engineer, you will play an important role in developing, deploying, and maintaining machine learning solutions that create real business value. You will work closely with data scientists, data engineers, and AI engineers to help bring models from development into production and ensure they perform reliably at scale. If you're passionate about applying machine learning to real-world challenges and want to help shape the future of AI at SAS, this is the role for you! As our Machine Learning Engineer, you will help develop and operationalize machine learning solutions across SAS. In this role, you will work hands-on with deployment, monitoring, and continuous improvement of production ML solutions. You will collaborate closely with data scientists to transition models from experimentation to production, help improve deployment processes and contribute to the ongoing development of our ML platform and engineering practices. Key Responsibilities Design, develop, test, deploy and monitor end-to-end pipelines for machine learning models in Azure. Apply MLOps best practices, including version control, continuous integration, continuous delivery, continuous training, testing, and monitoring, to ensure the quality and reliability of the machine learning solutions. Monitor model performance and help identify issues related to model quality, drift, and reliability. Support automation of model training, deployment, and retraining processes. Contribute to best practices for machine learning engineering, testing, documentation, and deployment. Work with Data Engineering and IT teams to ensure solutions are scalable, secure, and reliable. Help develop and improve CI/CD processes for machine learning applications. Participate in troubleshooting and resolving issues in production ML systems. Stay up to date with developments in machine learning, MLOps, and cloud technologies. The Team We are a central AI & Automation function within SAS Digital & IT that develops and operates solutions across machine learning, automation, MLOps, and Generative AI. Our team consists of data scientists, AI engineers, ML engineers, and automation specialists who work together to deliver solutions that create measurable business impact. As SAS continues to strengthen its AI capabilities, we are investing in modern cloud platforms, scalable AI solutions, and engineering practices that enable machine learning to create value across the business. As part of the team, you will contribute to a growing portfolio of machine learning products and collaborate across disciplines to ensure solutions are production-ready, scalable, and valuable to the business. To be successful we believe you have Master's Degree in Computer Science, Engineering, Machine Learning, Mathematics, or a related field. 2-5 years of experience in Machine Learning Engineering, MLOps, or a related role. Experience developing, deploying, and operating machine learning solutions in production environments. Hands-on experience with Azure ML, Azure Databricks, Azure Data Factory, or similar cloud-based ML platforms. Proficiency in Python, SQL, Git, and Bash. Experience deploying and serving machine learning models through APIs, as well as monitoring model performance in production. Understanding of model evaluation techniques, hyperparameter tuning, and validation approaches such as back-testing. Familiarity with Infrastructure as Code, preferably Terraform on Azure. Solid understanding of software engineering practices, including testing, version control, and CI/CD principles. Strong communication skills and the ability to explain and document technical concepts clearly and effectively. Experience with Generative AI, LLMs, or AI agents is considered a plus. We believe you are a curious and pragmatic engineer who enjoys solving problems with data and machine learning. You are comfortable working across the ML lifecycle, from experimentation to production, and enjoy collaborating with colleagues from different disciplines. You are eager to learn, improve your technical skills, and contribute to building reliable AI solutions that create real business value. Most importantly, you understand that successful machine learning is not only about models, but about delivering solutions that work in the real world and continue to provide value over time. Additional information Apply by November 16, 2026. We review applications continuously and encourage you to apply e

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