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.

Why SAS?

Join SAS at an exciting time of technological transformation. Digital & IT is modernizing our technology landscape, strengthening cloud-native capabilities, and expanding the use of AI across the company. As a Machine Learning Engineer, you will work with modern Azure technologies, MLOps practices, and Generative AI while building solutions that support both customer experiences and critical airline operations.

At SAS, we offer extensive opportunities for professional development in an international, fast-paced working environment. We are dedicated to the continuous growth of our employees. Working with us comes with a variety of benefits, including:

• Travel Perks: Enjoy discounted travel opportunities around the world with SAS.
• Health & Wellness: Access to health and wellness benefits, including a newly renovated gym with complimentary classes such as CrossFit and yoga.
• Discounts: Receive discounts from a wide range of brands, as well as on transportation to and from airports, airport shops, hotels, and car rentals.
• Work Environment: Our office location in Frösundavik offers a vibrant workspace with a restaurant, café, and easy access to outdoor activities in Hagaparken and Brunnsviken. Engage in running, tennis, outdoor gym sessions, kayaking, and stand-up paddling with equipment available free of charge.
• Convenient Commute: Benefit from a non-stop bus service connecting our office to Solna station, and commuter trains, alongside a network of cycle paths.

Our Culture at SAS

At SAS, we are dedicated to caring for each other, delighting our travelers, and driving the transformation towards sustainable aviation. As a future colleague on our team, you'll join a culture where we work collaboratively towards common goals, recognize each other's contributions, and celebrate successes. Our focus is on safe, sustainable, and punctual execution, and we are committed to protecting our planet while transforming SAS for the future. This is an empowering workplace where you can thrive, grow, and take ownership of your work. Join us at SAS and be part of shaping the future of aviation!

Additional information

• Apply by September 4, 2026. We review applications continuously and encourage you to apply early, as the position may be filled before the closing date. Please note that, due to GDPR regulations, we cannot accept applications via email.
• This is a full-time position (100%) based at our headquarters in Frösundavik, Solna, Stockholm. We value in-person collaboration, although flexible working arrangements may be available depending on team and business needs.
• As this role contributes to security-classified systems and business-critical capabilities, a background check will be conducted as part of the final stage of the recruitment process.
  
  

If you would like to learn more about the role, the team, or how AI, MLOps, and Generative AI are evolving at SAS, feel free to reach out to Warren Edgren, Head of AI & Automation, at warren.edgren@sas.se.

Please note that applications must be submitted through our careers site, as we are unable to process applications received by email.