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

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 + 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 + 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

Nice to Haves

Team Structure & Opportunities

Location & Work Arrangements

Critical Success Factors

About Our Client

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.

Compensation Package

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.