Join a team where you can turn advanced machine learning and modern software engineering into solutions that improve how payments operations run globally. You will partner closely with business and technology stakeholders to deliver measurable outcomes—faster decisioning, better risk detection, and smarter workflows—while building reliable, production-ready services.
As an Applied AI/Machine Learning Engineer at JPMorganChase within Wholesale Payments Operations, you will build, deploy, and continuously improve AI-enabled products that solve complex, high-impact business problems. You will translate ambiguous opportunities into clear technical approaches, develop models and services that perform in real production conditions, and help shape responsible, scalable adoption of modern AI. You will work in a collaborative environment that values ownership, learning, and delivering outcomes.
Job responsibilities
- Deliver end-to-end AI solutions by framing problems, defining success metrics, and shipping production releases that drive measurable operational impact
- Partner with operations, product, and engineering stakeholders to design workflows that augment decision-making and reduce manual effort
- Build and deploy cloud-based AI services with strong reliability, monitoring, and operational readiness
- Develop and maintain data and evaluation pipelines that support repeatable training, testing, and continuous improvement in production
- Apply machine learning techniques across text and documents (for example: natural language processing, classification, document understanding, and optical character recognition) to enable automation and decision support
- Design AI-enabled automation with appropriate safeguards by defining interfaces, controlling behavior, and measuring quality, latency, and cost tradeoffs
- Improve codebases through refactoring, testing, documentation, and engineering best practices to maintain long-term quality and maintainability
- Diagnose model and service performance issues, identify root causes, and implement improvements based on evidence and feedback loops
Required qualifications, capabilities, and skills
- Formal training or certification on applied artificial intelligence and machine learning concepts and 2+ years applied experience
- Bachelor’s degree in Computer Science, Engineering, or a related field (or equivalent practical experience)
- Hands-on Python experience building production-grade software (services, APIs, testing, and refactoring)
- Experience deploying services to cloud infrastructure and supporting production constraints (reliability, observability, and latency)
- Demonstrated ability to apply machine learning to real-world text and document use cases and evaluate model quality using clear metrics
- Strong software engineering fundamentals, including object-oriented design and concurrency concepts
- Experience driving work from problem definition through deployment and iteration, including communicating tradeoffs and progress to partners
- Experience using AI coding assistants responsibly to improve delivery speed while maintaining code quality, security-mindedness, and test coverage
- Strong communication skills and a collaborative working style
Preferred qualifications, capabilities, and skills
- Experience with managed machine learning platforms (for example: Amazon SageMaker or equivalent) for training and deployment workflows
- Experience building and operating large language model solutions, including structured evaluation approaches and guardrails
- Experience productionizing natural language processing and/or document understanding solutions at scale
- Experience designing AI-enabled automation workflows with measurable quality and ongoing monitoring in production
- Familiarity with prompt design and iterative improvement practices that make AI behavior reviewable and reproducible
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