AI ML Engineering Lead - Vice President - Wholesale Payments Operations

JPMorgan Chase & Co.•Jersey City, NJ

23h•$164,350 - $260,000

About The Position

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 - Vice President in the Wholesale Payments Operations team, you will build and deliver enterprise AI/ML solutions that improve operational efficiency and decisioning. You will partner with senior stakeholders to frame problems, define success metrics, and plan roadmaps. You will design, implement, and deploy production services on Amazon Web Services (AWS) with strong engineering rigor. You will establish model governance, monitoring, and responsible AI practices in line with risk and control expectations. You will 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 will contribute to reusable platforms and patterns that enable teams to safely deploy and operate models in production.

Requirements

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 (e.g., GitHub Copilot, Claude Code) to improve throughput 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

Nice To Haves

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

Responsibilities

Partner with senior business stakeholders to frame problems, define success metrics, and align AI/ML roadmaps to 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 (SDLC), continuous integration and continuous delivery (CI/CD), and DevOps practices
Deploy and operate AI/ML services on AWS at scale
Apply advanced techniques including data/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