Applied AI ML Director

Job Description

Are you passionate about harnessing the power of artificial intelligence and machine learning to solve real-world challenges? At JPMorgan

Chase, we're transforming the way payments work in the Commercial & Investment Bank by leveraging classical and cutting-edge AI/ML technologies.

As an Applied AI ML Director in the Commercial & Investment Bank at JPMorgan

Chase, you'll play a pivotal role in strategizing and building innovative solutions that enhance trust, safety, and operational efficiency for one of the world's leading financial institutions. You will own solutions end-to-end, from problem framing and data strategy to production deployment and measurement. You will remain hands-on while setting technical direction and partnering across product, engineering, data, risk, and compliance stakeholders.

• Demonstrated expertise in several areas from Graph Networks, Neural Networks, NLP, Vision, Classical ML and other technologies
• Domain expertise to develop and improve Trust & Safety problems in payment processing (e.g. Fraud Prevention, Authorization Optimization, Abuse).
• Own end-to-end delivery of problems in Payments (Trust & Safety or otherwise) solutions, from opportunity sizing and requirements through production rollout and iteration.
• Demonstrated ability to envision and develop AI/ML strategy that has platform wide impact within payments Organization.
• Define evaluation strategies and success metrics, including offline validation, error analysis, robustness testing, and controlled online measurement where appropriate.
• Establish model lifecycle practices including reproducibility, testing, monitoring, drift detection, and incident response to sustain reliable production performance.
• Partner with risk and compliance stakeholders to ensure appropriate documentation, controls, explainability expectations, and audit-ready processes.
• Drive technical decisions through design reviews, code and model reviews, and pragmatic standards that raise quality and delivery velocity.
• Communicate tradeoffs and recommendations to senior stakeholders, translating model behavior into decision-ready business impact.

• PhD in applied artificial intelligence, machine learning concepts or similar with 5+ years of experience or MS in applied artificial intelligence, machine learning concepts or similar with 8+ years experince.
• Experience building and delivering applied machine learning or natural language processing solutions with measurable outcomes in production.
• Strong programming skills in Python and experience using modern machine learning frameworks such as PyTorch or Tensor Flow.
• Hands‑on experience with document extraction and natural language processing techniques including text classification and information extraction.
• Experience designing data‑driven solutions using SQL and distributed processing tools such as Spark or equivalent.
• Experience deploying and operating machine learning services or pipelines in a cloud environment such as Amazon Web Services (or equivalent).
• Demonstrated ability to translate ambiguous business problems into structured machine learning plans, including data strategy, evaluation, rollout, and operationalization.
• Strong communication and collaboration skills, including the ability to explain technical tradeoffs to technical and non‑technical partners.

• Experience with optical character recognition and document understanding workflows for scanned or semi‑structured documents.
• Experience with modern natural language processing architectures such as transformer‑based models and techniques for optimization and efficient inference.
• Experience with machine learning operations practices and tooling, including model registries, continuous integration and delivery for machine learning, and observability.
• Experience with real‑time or event‑driven architectures supporting low‑latency inference and feature generation.
• Experience applying document extraction or natural language processing in payments, financial services, or regulated environments.

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