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About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
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As the AI ML Engineering Lead - Vice President - AI for Payments within the Wholesale Payments Operations team, you will build and deliver enterprise AI/ML solutions to improve operational efficiency and decisioning. You will lead the architecture, design, and end-to-end delivery of production-grade services involving natural language processing, document understanding, and LLM-enabled applications including retrieval-augmented generation and fine-tuning. Your daily work involves partnering with stakeholders to define roadmaps, writing production-quality code, and establishing MLOps practices like model governance and monitoring. You will utilize Python, AWS services such as SageMaker, Lambda, and S3, and tools like MLflow or Airflow. The role focuses on solving complex problems in wholesale payments by developing scalable capabilities for document processing, workflow automation, and data mining to support global client payments across various currencies and geographies.
Partner with senior stakeholders to define success metrics and align AI/ML roadmaps with business priorities.
Lead the architecture, design, and end-to-end delivery of enterprise AI/ML solutions for wholesale payments.
Develop production-grade code and establish high engineering standards for the team.
Deploy and operate scalable AI/ML services on AWS using modern CI/CD and DevOps practices.
Implement advanced techniques including NLP, OCR, document analysis, and LLM workflows like RAG and fine-tuning.
Build secure data pipelines to support model training and inference at scale.
Establish MLOps frameworks, model governance, monitoring, and responsible AI practices.
Mentor engineers through code reviews and lead talent development initiatives.
What we're looking for
Master’s degree in Computer Science, Engineering, Mathematics, or a related quantitative field.
6 years of professional software engineering experience delivering production systems.
4 years of advanced Python development in production environments, including use of AI-assisted coding tools.
4 years of hands-on experience designing and deploying production machine learning systems on Amazon Web Services (AWS).
Demonstrated experience building and deploying LLM-based applications, including retrieval-augmented generation and fine-tuning workflows.
Hands-on experience in natural language processing (NLP), computer vision, optical character recognition (OCR), or document AI solutions in production.
Experience implementing MLOps practices using tools such as MLflow, Kubeflow, Airflow, feature stores, or model registries.
Experience delivering AI/ML solutions in wholesale payments, transaction banking, or financial services (preferred); experience with model risk management frameworks and responsible AI practices (preferred).
Machine Learning
Deep Learning
Generative AI
LLMs
NLP
Python
Java
C++
Transformers
Hugging Face
TensorFlow
PyTorch
NumPy
Scikit-Learn
Pandas
AWS
SageMaker
Prompt Engineering
Big Data