Senior Python/PyTorch ML Engineer to lead production AI/ML model development and architect MLOps/ETL standardization across 100+ use cases B3617

Our Banking Client is seeking a Senior Python/PyTorch ML Engineer to lead the development of production AI/ML models for business units while architecting MLOps/AIOps standardization and ETL best practices across the enterprise. This strategic role will establish QA frameworks for ML systems, drive the Python/PyTorch standardization initiative across 100+ disparate use cases, and ensure production-ready model deployment for critical systems including chatbots, AML detection, predictive models (PRISM platform), and pricing optimization while maintaining quality, accuracy, and risk mitigation in a regulated environment.

• Lead development of production PyTorch models for The Bank's business units across retail banking, capital markets, and risk management• Architect MLOps/AIOps standardization frameworks for 100+ ML use cases ensuring consistency and scalability• Design and implement enterprise ETL pipelines for ML feature stores and data preprocessing at petabyte scale• Establish ML model QA best practices including testing frameworks, validation protocols, and performance benchmarks• Develop complex PyTorch implementations for LLMs, deep learning models, and advanced AI solutions• Lead the Python/PyTorch standardization initiative migrating legacy systems from diverse frameworks• Create production deployment strategies ensuring model reliability, monitoring, and governance• Design AIOps solutions for automated model monitoring, drift detection, and retraining pipelines• Architect scalable ETL workflows using Spark, Databricks, and cloud-native services• Establish ML engineering standards for code quality, documentation, and reproducibility• Provide technical leadership on MLOps best practices to development teams across the organization• Build reusable ML components and libraries in Python for enterprise-wide adoption• Define data quality frameworks and validation standards for ML pipelines• Translate complex business requirements into production ML solutions with stakeholder management• Mentor teams on PyTorch optimization techniques and production deployment patterns

• 7+ years Python programming with expert-level PyTorch experience for production ML systems

• Proven track record developing and deploying production ML models at enterprise scale

• Deep expertise in MLOps best practices and standardization including CI/CD, model versioning, and monitoring

• Extensive experience with ETL pipeline architecture for ML systems using Spark, Databricks, or similar

• Strong background in ML model QA methodologies and establishing testing frameworks

• Experience architecting AIOps solutions for model monitoring and automated retraining

• Expertise in cloud platforms (Azure or AWS) with production ML deployments using Kubernetes, Docker

• Proven ability to provide technical leadership on MLOps/AIOps best practices across teams

• Experience with Large Language Models (LLMs) implementation and deployment in PyTorch

• Strong understanding of deep learning architectures and optimization techniques

• Demonstrated ability to translate business requirements into production ML solutions with high EQ

• Experience working in regulated environments with focus on model governance and risk management

• Bachelor's degree in Computer Science, Engineering, Mathematics, or Physics (Master's preferred)

• Experience with TensorFlow as secondary framework (for migration purposes)

• Knowledge of Apache Airflow or Kubeflow for ML workflow orchestration

• Background in financial services industry, particularly banking or capital markets

• Experience with AML (Anti-Money Laundering) systems and regulatory compliance

• Familiarity with PRISM platform or similar predictive modeling systems

• Knowledge of real-time ML inference architectures and streaming pipelines

• Experience leading ML platform consolidation and migration initiatives

• Background in customer engagement strategy and marketing optimization models

• Experience with pricing models and financial risk modeling

• Understanding of data mesh or data fabric architectures

• Contributions to open-source ML/PyTorch projects

• Leadership experience or ability to direct the work of others

• French language skills (mandatory for Montreal-based position)

• Publications or presentations on MLOps best practices

• Multiple senior positions available across different teams

• Opportunities for both individual contributor and team lead roles

• Lead the Python/PyTorch standardization across AI/ML infrastructure

• Work with cutting-edge technologies on high-impact production models

• Remote Options: 100% remote available for select positions

• Hybrid Arrangements: Flexible work options for Toronto-based roles

• Montreal Position: Requires bilingual (French/English) skills with 4 days/week in-office requirement

• Expert-level Python/PyTorch skills for production ML development

• Deep understanding of MLOps/AIOps best practices and ability to establish standards

• Experience with ETL pipeline architecture and data engineering for ML

• High EQ with exceptional stakeholder management and communication skills

• Ability to prioritize quality, accuracy, and risk management over rapid prototyping

• Experience guiding teams through ML platform standardization initiatives

• Current knowledge of production ML deployment patterns and best practices

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.