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About the Role
Drive innovation in the fast-evolving payments space by working at the forefront of modern machine learning and generative AI to deliver meaningful, lasting impact on global finance. Partner closely with product, operations, risk, and technology teams to turn ideas into measurable outcomes, with opportunities to mentor others and shape AI development and operations.
Responsibilities
- Lead end-to-end delivery of machine learning and AI solutions for complex payments and banking operations problems, from discovery and framing to production rollout and lifecycle management
- Develop innovative machine learning solutions, including generative AI and multi-agent approaches, and define evaluation, safety, and monitoring strategies for production use
- Own production deployment patterns including containerization, continuous integration and delivery, automated testing, model and prompt registries, model and version governance, monitoring and alerting, and rollback strategies
- Architect and deploy scalable, reliable, and secure machine learning and large language model services integrated with strategic platforms and downstream consumers across APIs, batch, streaming, and event-driven patterns, meeting service level objectives
- Partner with product, operations, risk and control, and technology teams to influence roadmaps, align on requirements, and deliver data-led transformations
- Establish reusable, modular data science and machine learning capabilities that scale across use cases, including feature engineering, evaluation harnesses, prompt tooling patterns, agent frameworks, orchestration, and context and memory management
- Provide technical leadership and mentorship through code reviews, design reviews, best practices, and upskilling across data science and engineering partners
- Communicate with technical and non-technical stakeholders, translating model outputs into decisions, tradeoffs, and operational plans
- Maintain strong documentation for approaches, model cards, runbooks, and operational procedures
Requirements
- Master’s degree in a quantitative field, or equivalent practical experience
- Deep understanding of machine learning fundamentals with strong applied data analysis skills
- Demonstrated experience designing rigorous evaluation and measurement in real-world settings
- Demonstrated experience deploying and operating machine learning models in production at scale, including monitoring, drift and performance management, reliability, incident management, and continuous improvement
- Strong Python software engineering skills, including modular object-oriented design, testing, performance tuning, and debugging
- Working knowledge of MLOps and LLMOps and distributed systems, including training and serving patterns, batch versus real-time architectures, feature stores, orchestration, and scalable data processing
- Ability to design intrinsic and extrinsic evaluations aligned with business goals, including offline and online alignment and guardrails for unintended outcomes
- Experience working in regulated environments with awareness of model risk, controls, privacy and security, and audit-ready documentation
- Strong stakeholder management and teamwork skills, with the ability to drive outcomes in partnership with cross-functional teams
Skills
- Machine Learning
- Generative AI
- Agentic systems
- Cloud infrastructure
- MLOps
- LLMOps
- Python
- Object-oriented design
- Testing
- Performance tuning
- Debugging
- Distributed systems
- Training and serving patterns
- Batch architectures
- Real-time architectures
- Feature stores
- Orchestration
- Scalable data processing
- Intrinsic evaluations
- Extrinsic evaluations
- Model risk
- Controls
- Privacy
- Security
- Audit-ready documentation
- Stakeholder management
- Teamwork
- NLP
- Large language models
- Retrieval-augmented generation
- Tool and function calling
- Multi-agent orchestration
- PyTorch
- TensorFlow
- scikit-learn
- NumPy
- pandas
- SciPy
- statsmodels
- AWS
- SageMaker
- Bedrock
- Human-in-the-loop
- User feedback signals
- Active learning
- Preference signals
- Labeling strategies
Location
- Global
Work Type
- Full-time
Experience Level
- Vice President
- Senior
Education Level
- Master's degree in a quantitative field or equivalent practical experience
About the Company
- J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world’s most prominent corporations, governments, wealthy individuals and institutional investors.
- Our first-class business in a first-class way approach to serving clients drives everything we do. We strive to build trusted, long-term partnerships to help our clients achieve their business objectives.
- We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success.
- 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.
Equal Opportunity
- We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs [https://careers.jpmorgan.com/us/en/how-we-hire/faqs] for more information about requesting an accommodation.