Applied AI ML - Payments Machine Learning (Associate)

J.P. Morgan’s Commercial & Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world. 

Join us at the forefront of payments innovation, where your expertise in machine learning and generative AI will help shape how money moves worldwide. You will collaborate with diverse teams to deliver impactful solutions, advancing your career in a dynamic and fast-evolving environment. We value your creativity, technical skills, and drive to make a measurable difference. At JPMorganChase, you’ll find opportunities for growth, learning, and meaningful contribution. Together, we’re building the future of payments.

As an Associate Machine Learning Data Scientist in Payments Machine Learning, you will design, develop, and deploy machine learning applications—including generative AI—on cloud infrastructure. You will contribute across the project lifecycle, partnering with senior engineers and data scientists to ensure solutions are reliable, secure, and observable in production. You will communicate progress and results to stakeholders, maintain clear documentation, and help drive innovation in payments and banking operations.

Job Responsibilities:

• Deliver machine learning and AI solutions for payments and banking operations, from discovery to production rollout
• Apply agentic engineering practices to build LLM-powered workflows and evaluate their quality, safety, and reliability
• Contribute to deployment workflows including containerization, CI/CD, automated testing, versioning, monitoring, and rollback procedures
• Develop scalable and secure ML/LLM services integrated with strategic platforms and downstream consumers
• Partner with product, operations, risk and control, and technology teams to clarify requirements and deliver data-led improvements
• Build reusable components such as feature engineering pipelines, evaluation harnesses, and orchestration patterns
• Participate in code and design reviews; contribute to best practices, documentation, and team standards
• Communicate with technical and non-technical stakeholders, translating model outputs into practical decisions
• Maintain documentation such as model cards, runbooks, experiment notes, and operational procedures

Required Qualifications, Capabilities, and Skills:

• Relevant industry experience in applied machine learning, data science, ML engineering, or related roles
• Bachelor’s or Master’s degree in a quantitative field or equivalent practical experience
• Strong understanding of machine learning fundamentals and applied data analysis skills
• Experience designing evaluations and measuring impact in real-world settings
• Experience deploying and operating ML models or ML-enabled services in production, including monitoring and troubleshooting
• Strong Python software engineering skills, including modular code, testing, debugging, and performance awareness
• Working knowledge of ML engineering/MLOps concepts, including training vs. serving, batch vs. real-time, orchestration, scalable data processing, and familiarity with model/prompt versioning and governance
• Ability to align evaluation and guardrails to business goals and identify potential unintended outcomes
• Experience operating in regulated or control-conscious environments with attention to model risk, privacy, security, and audit-ready documentation
• Strong stakeholder management and teamwork skills, with the ability to drive outcomes in cross-functional teams

Preferred Qualifications, Capabilities, and Skills:

• Hands-on experience with NLP and/or generative AI, including LLMs, RAG, tool/function calling, and agentic workflows
• Familiarity with agentic building blocks such as orchestration frameworks and context/memory patterns; awareness of interoperability approaches
• Experience deploying to AWS (e.g., SageMaker and/or Bedrock) and operating production ML/LLM workloads with attention to cost, latency, performance, security, and scaling
• Experience integrating human-in-the-loop review and user feedback into iterative improvement, such as labelling strategies, QA workflows, and preference signals