AI ML Engineering Analyst

JPMorganChase runs the worlds 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. Youll 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.

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

Required qualifications capabilities and skills

• Bachelors 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.

Preferred qualifications capabilities and skills

• 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.