Lead Applied AI ML for Payments

JPMorgan Chase; Jersey City (NJ); On-site; USD 170,000 - 230,000; Full time

Job summary

JPMorgan Chase is seeking a senior AI/ML engineer to join a team delivering high-scale AI/ML solutions for Wholesale Payments Operations. You will frame problems with business stakeholders, design end-to-end architectures, and write production-grade Python code.

You will deploy services on AWS, implement NLP/OCR/document AI, and drive MLOps, governance, and responsible AI practices while mentoring engineers and collaborating with partners across the business.

Qualifications

• Master's degree required in a quantitative field.
• 6+ years of professional AI/ML experience delivering production systems.
• 4+ years of advanced Python development in production environments.
• 4+ years deploying production ML systems on AWS (SageMaker, Lambda, ECS/EKS, S3).
• Experience delivering AI/ML solutions with measurable business outcomes at scale.
• Experience with OO design, distributed systems, and performance engineering.
• Experience building deploying LLM-based apps incl. RAG and fine-tuning.
• Hands-on NLP, OCR, or document AI production experience.
• MLOps practices with tools like MLflow, Kubeflow, Airflow, feature stores, or model registries.

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, production-quality code and set engineering standards across the team
• Champion modern SDLC, CI/CD, and DevOps practices
• Deploy and operate AI/ML services on AWS at scale
• Apply data/text mining, document analysis, NLP, OCR, and LLM workflows (RAG & fine-tuning)
• Design scalable, secure data pipelines for model training and inference
• Define and enforce MLOps, model governance, monitoring, and responsible AI; participate in risk forums
• Evaluate production model performance, drift management, reproducibility
• Mentor engineers, conduct reviews, support recruiting and talent development

Skills

Python development

AWS (SageMaker, Lambda, ECS/EKS, S3)

NLP / OCR / Document AI

LLM workflows (RAG, fine-tuning)

MLOps tooling (MLflow, Kubeflow, Airflo

Mentoring / leadership

Education

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

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.

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