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Accountabilities:
- Drive and continuously improve MLOps practices across the machine learning environment.
- Build, optimize, and maintain CI/CD pipelines using GitLab to automate reliable ML delivery.
- Productionize, deploy, and maintain machine learning models using AWS, with a strong focus on SageMaker.
Requirements:
- 5+ years of professional experience in Machine Learning Engineering or a closely related field.
- Expert-level Python skills and strong knowledge of the data science and ML ecosystem.
- Hands-on experience with AWS cloud services, preferably including AWS SageMaker.
Benefits:
- B2B contract arrangement with opportunity to work on technically challenging ML projects.
- Exposure to modern ML technologies, AWS infrastructure, MLOps tooling, and enterprise-scale systems.
- Collaborative and supportive environment focused on knowledge sharing and professional development.
Partner Company
The company operates a globally deployed recommender system and focuses on machine learning infrastructure. The team is collaborative and supportive, with an emphasis on knowledge sharing and professional development.