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.
