AI ML Engineering Analyst

JPMorgan Chase & Co.•Jersey City, NJ

About The Position

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.

Requirements

Bachelor's degree in Computer Science or a related field.
2+ years of hands-on Python experience with a proven ability to build production-grade software (APIs/services, testing, refactoring).
1+ year of hands-on experience deploying to cloud infrastructure (AWS or equivalent) and working within production constraints (latency, reliability, observability).
Strong object-oriented design and concurrency fundamentals.
Practical experience applying AI/ML techniques (e.g., text mining, document analysis, classification, OCR) and evaluating model quality in real-world settings.
Track record of independently driving solutions from problem framing through deployment and iteration, with measurable outcomes.
Proficiency using AI coding tools (e.g., GitHub Copilot, Claude Code) to increase development throughput while preserving code quality.
Working knowledge of prompt engineering: ability to design, test, and iterate on prompts for repeatable, high-quality AI outputs.
Strong communication skills and a collaborative, team-first working style.

Nice To Haves

AWS (or equivalent) beyond basics, including managed ML platforms such as SageMaker (or equivalent) for training and deployment workflows.
Experience building LLM-powered solutions, including designing agentic workflows with measurable evaluation and guardrails.
Track record of accelerating delivery using AI-assisted development while maintaining high engineering standards (tests, refactoring discipline, production readiness).
Experience productionizing NLP and/or document understanding solutions at scale.

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.
Analyze and evaluate ongoing model and service performance, diagnose failure modes, and drive continuous improvements.