Machine Learning Engineer jobs in Canada

Machine Learning Engineer

This is with a large IT services firm for a leading US based bank.
Candidate must have legal work status for Canada.
Client Location is Mississauga, Ontario (near Hwy 401 and Hwy 10)
Hybrid - In-Office 3 days per week.
Perm/FT role, salary and benefits,

Job Description:
We are seeking a skilled Machine Learning Engineer with expertise in Spark ML, predictive modeling, and deploying training and inference pipelines on distributed systems such as Hadoop. The ideal candidate will design, implement, and optimize machine learning solutions for large-scale data processing and predictive analytics.

Responsibilities:
• Develop and implement machine learning models using Spark ML for predictive analytics.
• Design and optimize training and inference pipelines for distributed systems (e.g., Hadoop).
• Process and analyze large-scale datasets to extract meaningful insights and features.
• Collaborate with data engineers to ensure seamless integration of ML workflows with data pipelines.
• Evaluate model performance and fine-tune hyperparameters to improve accuracy and efficiency.
• Implement scalable solutions for real-time and batch inference.
• Monitor and troubleshoot deployed models to ensure reliability and performance.
• Stay updated with advancements in machine learning frameworks and distributed computing technologies.

Requirements:
• 10+ years’ experience with 5 yrs as a Machine Learning Engineer.
• Proficiency in Apache Spark and Spark MLlib for machine learning tasks.
• Strong understanding of predictive modeling techniques (e.g., regression, classification, clustering).
• Experience with distributed systems like Hadoop for data storage and processing.
• Proficiency in Python, Scala, or Java for ML development.
• Familiarity with data preprocessing techniques and feature engineering.
• Knowledge of model evaluation metrics and techniques.
• Experience with deploying ML models in production environments.
• Understanding of distributed computing concepts and parallel processing.