Senior Python​/PyTorch ML Engineer to lead production AI​/ML model development and architect MLOps​/ETL

Location: Verdun South

Overview     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  .
Responsibilities     
• 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

Must Haves     
• 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 Py Torch

• 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)

Nice to Haves     
• Experience with  Tensor Flow   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…