Job Title : Machine Learning Scientist / Engineer
Length of contract- 12 Months (Possible extension)
Start date : ASAP
Location : Toronto, ON
Hybrid- 2-3 days a week @ Toronto, ON
Interview- 2 rounds (first panel, 45 min)
Machine Learning Scientist / Engineer
We are seeking a highly skilled Machine Learning Scientist / Engineer with deep expertise in designing, building, and deploying scalable ML solutions. The ideal candidate will have hands-on experience across the full machine learning lifecycle, including model development, optimization, and production deployment in cloud environments.
Responsibilities
- Design and implement machine learning models using Python , PyTorch , TensorFlow , and Keras , applying both parametric and non-parametric approaches.
- Develop and optimize deep learning architectures, including CNNs , for complex tasks.
- Build robust CI / CD pipelines for ML systems using tools like Jenkins , GitHub Actions , and ML flow to enable automated testing, deployment, and monitoring.
- Containerize ML applications with Docker and manage deployments using Kubernetes .
- Implement scalable data processing and streaming solutions leveraging Kafka and distributed systems.
- Collaborate with cross-functional teams to deploy ML solutions into production with high reliability and performance.
- Monitor, detect, and address model drift, pipeline drift, and long-term model degradation.
Qualifications
Strong programming skills in Python and proficiency with major ML frameworks ( PyTorch , TensorFlow , Keras ). Proficiency in SQL for data querying, preprocessing, and analytical workflows.Hands-on experience with CI / CD , Dockerization , and Kubernetes for ML workflows.Deep understanding of modeling techniques, including parametric / non-parametric methods and CNN-based architectures .Knowledge of data lineage , pipeline drift , and model drift monitoring .Experience with distributed systems and streaming platforms such as Kafka .Proven ability to design end to end, scalable, production ready ML systems .Familiarity with data lineage, experiment tracking, and model versioning using tools such as MLflow or DVC.Strong understanding of statistical modeling and feature engineering techniques , including correlation analysis, PCA, dimensionality reduction, and basic feature selection methods.