Lead Applied AI ML for Payments

JPMorgan Chase & Co.; Jersey City (NJ); On-site; USD 180,000 - 240,000; Full time

Job summary

JPMorgan Chase in Jersey City is seeking an Applied AI/ML Lead for Wholesale Payments Operations. You will build and deploy enterprise AI/ML solutions on AWS, mentor engineers, and collaborate with senior stakeholders to frame problems and plan roadmaps.

The role focuses on scalable AI/ML capabilities for payments operations, including document processing and workflow automation, with governance and risk considerations integral to delivery.

Qualifications

• 6+ years of professional AI/ML experience delivering production systems.
• 4+ years Python development in production with AI-assisted tooling.
• 4+ years deploying production ML on AWS.
• Proven track record delivering AI/ML at scale with measurable outcomes.
• Experience with distributed systems, object-oriented design, and performance engineering.
• Experience deploying LLM-based apps including retrieval-augmented generation and fine-tuning.
• Hands-on NLP, OCR, or document AI in production.
• Experience implementing MLOps using MLflow, Kubeflow, Airflow, or feature stores.

Responsibilities

• Frame problems with senior stakeholders and align AI roadmaps.
• Lead architecture and end-to-end delivery of enterprise AI/ML solutions.
• Write clean, production-grade code and set standards.
• Champion CI/CD and DevOps practices across the team.
• Deploy and operate AI/ML services on AWS at scale.
• Define model governance, monitoring, and responsible AI practices.
• Mentor engineers, conduct reviews, and support recruiting.
• Evaluate model performance and drift in production.

Skills

AI/ML leadership

Python development

Production ML systems

AWS production services

Model governance & risk

NLP/OCR/Document AI

MLOps tooling (MLflow/Kubeflow/Airflow

Data pipelines

Mentoring engineers

Strong stakeholder communication

Education

Master’s degree in Mathematics, Computer Science, Engineering, or related field

Tools

AWS SageMaker

AWS Lambda

AWS ECS/EKS

AWS S3

Kubeflow

MLflow

Airflow

Job description

Join a team applying modern artificial intelligence and machine learning to high-impact, high-scale payments workflows. You will work with large datasets and complex operational processes to deliver measurable outcomes. You will build production-grade solutions spanning natural language processing, document understanding, and LLM-enabled applications. You will collaborate closely with business and technology partners to take ideas from concept to deployment. You will help raise engineering standards and mentor others while shipping real solutions.

As an Applied AI/ML - Lead in Wholesale Payments Operations within Commercial & Investment Bank in JPMorgan Chase, you build and deliver enterprise AI/ML solutions that improve operational efficiency and decisioning. You partner with senior stakeholders to frame problems, define success metrics, and plan roadmaps. You design, implement, and deploy production services on Amazon Web Services (AWS) with strong engineering rigor. You establish model governance, monitoring, and responsible AI practices in line with risk and control requirements. You mentor engineers and lead reviews that improve quality, reliability, and delivery speed.

Wholesale Payments supports global client payments across multiple methods, currencies, and geographies. The role focuses on building scalable AI/ML capabilities for operations use cases, including document processing and workflow automation. You contribute to reusable platforms and patterns that enable teams to safely deploy and operate models in production.

Job responsibilities

• Partner with senior business stakeholders to frame problems, define success metrics, and align AI/ML roadmaps to business priorities
• Lead architecture, design, and end-to-end delivery of enterprise AI/ML solutions for Wholesale Payments Operations
• Write clean, performant, production-quality code and set engineering standards across the team
• Champion modern software development life cycle, continuous integration and continuous delivery, and DevOps practices
• Deploy and operate AI/ML services on AWS at scale
• Apply advanced techniques including data and text mining, document analysis, classification, optical character recognition (OCR), natural language processing (NLP), and LLM workflows (including retrieval-augmented generation and fine-tuning)
• Design and implement scalable, secure data pipelines to support model training and inference
• Define and enforce MLOps, model governance, monitoring, and responsible AI practices; represent the team in architecture and risk forums
• Evaluate model performance in production, including drift management and reproducibility
• Mentor engineers, conduct code and design reviews, and support recruiting and talent development

Required qualifications, capabilities, and skills

• Master’s degree in Mathematics, Computer Science, Engineering, or a related quantitative field
• 6 years of professional AI/ML experience delivering production systems
• 4 years of advanced Python development in production environments, including use of AI-assisted coding tools to improve productivity while preserving code quality
• 4 years of hands‑on experience designing and deploying production machine learning systems on Amazon Web Services (AWS) (for example: SageMaker, Lambda, ECS/EKS, S3)
• Demonstrated experience delivering AI/ML solutions with measurable business outcomes at scale
• Experience with object-oriented design, distributed systems, and performance engineering
• 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
• Demonstrated experience mentoring engineers and driving execution against multi‑quarter roadmaps
• Strong communication skills, including translating business needs into technical deliverables for senior stakeholders

Preferred qualifications, capabilities, and skills

• Experience delivering AI/ML solutions in wholesale payments, transaction banking, or financial services
• Experience with model risk management frameworks, model governance, and responsible AI practices
• Experience with Kubernetes and infrastructure‑as‑code (for example: Terraform)
• Experience with real‑time or streaming inference use cases
• Contributions to open‑source machine learning ecosystems or peer‑reviewed publications