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

Full Description

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

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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…