• Driving and improving MLOps practices across the ML environment
• Building and optimizing CI/CD pipelines using GitLab
• Implementing ML experiment tracking and model management with MLflow
• Productionizing and deploying machine learning models using AWS SageMaker
• Designing and maintaining scalable ML and data pipelines
• Developing and maintaining Python-based ML and data infrastructure
• Implementing monitoring and observability for ML systems
• Providing technical guidance and mentoring to Data Scientists, Data Engineers, and MLOps Engineers
• Applying software engineering best practices, including testing, documentation, and system design
• Collaborating with Product Managers, Data Scientists, Engineers, and business stakeholders
• Evaluating and introducing new technologies to improve ML capabilities

Requirements

• 5+ years of professional experience in Machine Learning Engineering
• Strong experience deploying and maintaining production ML systems
• Expert-level Python skills and knowledge of the data science ecosystem
• Hands-on experience with AWS, preferably AWS SageMaker
• Strong knowledge of MLOps practices and lifecycle
• Practical experience with MLflow
• Experience with GitLab CI/CD
• Experience with at least one major deep learning framework, e.g. PyTorch or TensorFlow
• Experience designing and building scalable ML and data pipelines
• Experience with ML system monitoring and observability