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

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

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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

• 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 financial risk modeling
• Understanding of data mesh or data fabric architectures
• Contributions to open-source ML/PyTorch projects
• Leadership experience…