MLOps with AWS Sagemaker

We are looking for an experienced Senior Machine Learning Engineer to join a global team responsible for a production-grade recommendation platform used across multiple international markets. The solution is already delivering significant business value and is entering its next stage of evolution, with a strong focus on scalability, reliability, and operational excellence.

This is a senior, hands-on engineering role where you will shape the architecture of a mature ML platform, strengthen the organization's MLOps capabilities, and work closely with Data Scientists to bring machine learning models into production.

The ideal candidate combines strong software engineering skills with deep expertise in Machine Learning Engineering and MLOps, particularly within the AWS ecosystem.

. Design and evolve the architecture of a large-scale production Machine Learning platform., Build, maintain, and optimize end-to-end MLOps pipelines., Deploy, monitor, and maintain machine learning models using AWS SageMaker., Develop and improve CI/CD pipelines for ML workflows using GitLab CI/CD., Manage experiment tracking, model versioning, and reproducibility with MLflow., Collaborate closely with Data Scientists to productionize machine learning models., Design scalable and reliable ML infrastructure following software engineering best practices., Implement monitoring, observability, and model performance tracking solutions., Write clean, maintainable, and well-tested Python code., Mentor engineers and Data Scientists on MLOps, software engineering, and production deployment best practices., Participate in architectural discussions and contribute to technical decision-making.. 5+ years of professional experience as a Machine Learning Engineer, Senior MLOps Engineer, or in a similar role., Strong experience building and operating production Machine Learning systems., Hands-on experience with AWS SageMaker (training, deployment, endpoints, pipelines)., Advanced Python programming skills., Strong knowledge of the complete MLOps lifecycle., Commercial experience with MLflow., Experience building CI/CD pipelines using GitLab CI/CD., Practical experience with at least one deep learning framework: TensorFlow PyTorch, Experience deploying and maintaining ML models in cloud environments (AWS preferred)., Solid understanding of Machine Learning model lifecycle, model serving, and monitoring., Ability to design scalable ML architectures and production-ready data pipelines., Experience mentoring engineers or providing technical guidance., Strong communication skills and the ability to work with cross-functional teams., Experience with recommender systems., Experience with model monitoring and observability tools such as Prometheus, Grafana, or Evidently AI., Docker and Kubernetes experience., Experience with Infrastructure as Code (Terraform or Cloud

Formation)., MSc or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field.