data
Posted Apr 1Machine Learning Engineer
at Bree
CanadaRemote
Responsibilities
- - Implement MLOps best practices, including CI/CD for ML models, model versioning, monitoring, and retraining strategies.
- - Collaborate with data engineers to develop high-performance data pipelines for training and inference.
- - Maintain model performance by implementing continuous monitoring, bias detection, and explainability techniques.
Requirements
- ABOUT THE ROLE We’re looking for a Machine Learning Engineer to build and scale high-impact, world-class ML systems.
- You’re passionate about deploying AI solutions, optimizing performance, and driving measurable results.
- WHAT YOU'LL DO - Design, develop, and deploy end-to-end machine learning pipelines, ensuring efficiency in training, validation, and inference.
- - Optimize ML models using feature engineering, hyperparameter tuning, and scalable inference techniques.
- - Work with structured and unstructured data, leveraging Pandas, NumPy, and SQL for efficient data manipulation.
- - Apply machine learning design patterns to build modular, reusable, and production-ready models.
- - Deploy and manage models on cloud platforms (AWS, GCP, Azure) with containerization and orchestration tools like Docker and Kubernetes.
- WHAT YOU'LL NEED - Proficiency in Python and familiarity with ML libraries like Scikit-learn, LightGBM, and PyTorch. - Strong understanding of machine learning algorithms, including supervised and unsupervised learning techniques. -
- Experience with MLOps tools such as MLflow, Kubeflow, or SageMaker for tracking experiments and automating workflows. - Hands-on
- experience with data manipulation libraries (Pandas, NumPy) and databases (SQL, NoSQL). - Knowledge of cloud-based ML deployment and infrastructure management. - Ability to implement real-time and batch inference pipelines efficiently. - Strong analytical and problem-solving skills to translate business needs into scalable ML solutions. - Eagerness to work in a fast-paced environment and continuously refine ML processes for efficiency and accuracy.