AI ML Engineering Analyst

JPMorganChase runs the world's largest wholesale payments network across Treasury Services, Merchant Services, Trade, and Commercial Card, enabling clients to pay globally in any currency and payment method.

It delivers an end-to-end suite spanning Payments, Liquidity, Trade, and Finance, supported by real-time insights and expert advice.

This role is in Applied AI and Machine Learning, partnering closely with Wholesale Payments Operations, which processes over 106 million transactions worth $6 trillion daily across 120+ currencies and receives payments in 40+ countries.

As an AI/ML Engineer in Wholesale Payments Operations, you will design, implement, and deploy high-quality solutions for the complex business problems we face at JPMorganChase.

We have rewarding technical challenges, large data sets, and a tremendous opportunity for innovative AI/ML work, including NLP, document understanding, agentic system design, and AI-assisted development.

You'll draw on strong software engineering fundamentals and modern AI techniques to deliver commercially impactful, production-grade solutions.

The ideal candidate will have a deep understanding of design patterns, Python programming, cloud infrastructure, and the emerging discipline of prompt engineering and AI-augmented development.

We're looking for enthusiastic, bright, and personable people with strong communication skills, a collaborative working style, and a passion for shipping real AI solutions.

We value people who take ownership, seek feedback, and make the team around them better.

Job responsibilities


* Learn Wholesale Payments Operations workflows deeply, identify high-impact opportunities, and translate ambiguous problems into clear solutions with measurable outcomes.


* Design, implement, and deploy AI/ML services to cloud infrastructure with production-quality reliability, monitoring, and operational readiness.


* Build and maintain data pipelines that enable repeatable training, evaluation, and continuous improvement of models in production.


* Apply AI/ML techniques across text and documents (e.g., NLP, document analysis, text/image classification, OCR) to create automated decisioning and workflow augmentation solutions.


* Use AI coding assistants effectively (e.g., GitHub Copilot, Claude Code, or firm-approved equivalents) to accelerate delivery while maintaining engineering rigor: readability, tests, security-mindedness, and maintainability.


* Prompt engineer and iterate systematically: write, test, and refine prompts; develop evaluation strategies; and document prompt patterns to make AI behaviors reproducible and reviewable.


* Design agentic systems where appropriate: decompose tasks, define tool interfaces, add safeguards, and measure quality/latency/cost tradeoffs to ensure controllable, production-ready automation.


* Refactor code, write tests, and uphold code quality metrics so models and services remain robust as products scale.


* An...