Junior Python/PyTorch ML Engineer to develop production AI/ML models and standardize MLOps/ETL pipelines

Our Client is seeking a Junior Python/PyTorch ML Engineer to develop production-ready AI/ML models for business units while contributing to the standardization of MLOps/AIOps and ETL pipelines across the enterprise. This role will build and deploy PyTorch-based models for critical systems including chatbots, AML detection, and predictive analytics while helping establish QA best practices for ML systems across 100+ use cases.

• Develop production-ready PyTorch models for The Banking business units including retail banking, capital markets, and risk management• Build Python-based ML pipelines with focus on scalability and production deployment• Implement MLOps best practices for model versioning, monitoring, and deployment using Databricks and Kubernetes• Design and optimize ETL pipelines for ML feature engineering and data preprocessing at scale• Create model QA frameworks including unit tests, integration tests, and performance benchmarks• Develop PyTorch implementations for LLMs, computer vision, and time-series forecasting models• Establish AIOps monitoring solutions for model drift detection and performance tracking• Build standardized ML components that integrate with PRISM platform and Layer 6 implementations• Implement data quality checks and validation frameworks for ML pipelines• Contribute to MLOps standardization initiatives across 100+ use cases• Support production model deployment with proper logging, monitoring, and alerting• Document best practices for ML QA and contribute to enterprise ML standards

• 2-3+ years Python programming with strong expertise in PyTorch for production ML models• Hands-on experience developing and deploying ML models to production environments• Experience with MLOps practices including model versioning, CI/CD pipelines, and monitoring• Knowledge of ETL pipeline development for ML feature engineering and data preprocessing• Understanding of ML model QA methodologies including testing, validation, and performance benchmarking• Experience with cloud platforms (Azure or AWS) and ML services like Databricks, Azure Machine Learning• Proficiency in containerization (Docker, Kubernetes) for ML model deployment• Strong foundation in Machine Learning fundamentals and deep learning architectures• Experience with data engineering tools and practices for ML pipelines• Bachelor's degree in Computer Science, Engineering, Mathematics, or Physics

• Experience with AIOps platforms and automated monitoring solutions• Knowledge of Apache Spark and distributed computing for ETL• Familiarity with TensorFlow as secondary framework• Experience with Large Language Models (LLMs) and transformer architectures in PyTorch• Background in financial services or other regulated industries• Knowledge of ML model governance and compliance frameworks• Experience with Apache Airflow or similar workflow orchestration tools• Understanding of optimization and operations research methodologies• Familiarity with AML (Anti-Money Laundering) systems or fraud detection• Experience with real-time ML inference and streaming architectures• Knowledge of data quality frameworks and data governance• French language skills (for Montreal-based positions)

• Toronto/Remote Options: Hybrid or remote arrangements available for most positions• Montreal Positions: Require bilingual (French/English) skills with 4 days/week in-office

Our Client is one of the world's leading global financial institutions and the fifth largest bank in North America. We deliver legendary customer experiences to over 27 million households and businesses. As we build our business and deliver on our strategy, we are innovating to enhance the customer experience and shape the future of banking.

Our package includes competitive base salary, variable compensation, comprehensive health and well-being benefits, savings and retirement programs, paid time off, banking benefits and discounts, extensive career development opportunities, and reward and recognition programs.