Principal Technical Program Manager- Ai/ml- Payments

JPMorgan Chase JPMorgan Chase · Banking · New York, NY +1 · Commercial & Investment Bank

This role is for a Principal Technical Program Manager to lead the end-to-end delivery of a Large Payments Model (LPM), a domain-specific foundation model trained on structured payments data for prediction and classification tasks within JPMorgan Chase's Payments Technology Team. The role involves coordinating Applied AI/ML, engineering, data pipelines, and governance/controls to ensure successful training, serving, integration, and adoption of the model.

What you'd actually do

  1. Own the program plan for LPM delivery: milestones, critical path, dependencies, resourcing, and operating cadence across multiple teams.
  2. Oversee execution across resources, timelines, and budgets (as applicable); manage dependencies and control change in high-pressure, shifting environments.
  3. Drive alignment on LPM’s delivery model and integration patterns for downstream consumers: real-time inference services, batch scoring, streaming integrations, and feature/signal delivery, including versioning and contract management.
  4. Coordinate the Science track: training data/label strategy, model objectives, evaluation gates, robustness/segmentation, calibration, explainability inputs (as required), and model release criteria.
  5. Coordinate the Production track: scalable data pipelines, model packaging, CI/CD, serving reliability, observability/monitoring, incident/rollback readiness, and cost/latency performance.

Skills

Required

  • 7+ years experience in technical program management (or engineering + program leadership) delivering complex, cross-functional initiatives from design through production launch.
  • Strong technical fluency across modern systems: distributed services, data pipelines, integration contracts (schemas, SLAs/SLOs, versioning), and operational readiness (monitoring, incident response).
  • Experience delivering data/ML-enabled systems (you are not expected to build models, but must understand ML lifecycle concepts such as training data/labels, evaluation, deployment patterns, monitoring/drift, and feedback loops).
  • Demonstrated ability to lead execution across Applied AI/ML, engineering, data, and control partners in a high-governance environment.
  • Excellent written/verbal communication and executive-ready program reporting; strong ownership and ability to drive decisions under ambiguity.
  • Experience working with vendor/partner solutions and/or platform teams, including evaluating options and managing delivery dependencies (as applicable).

Nice to have

  • Payments/fintech domain exposure (transaction processing, fraud/risk, payment optimization, merchant/treasury services).
  • Experience with multi-tenant platform delivery (serving multiple downstream teams/products with versioning, SLAs, and backward compatibility).
  • Familiarity with cloud-native delivery, CI/CD, and observability (e.g., AWS; streaming and batch architectures).
  • Experience working with model governance, risk/compliance, and audit requirements.

What the JD emphasized

  • Large Payments Model (LPM)
  • structured payments data
  • Applied AI/ML
  • engineering
  • data/feature pipelines
  • governance/controls
  • prediction and classification
  • real-time inference services
  • batch scoring
  • streaming integrations
  • feature/signal delivery
  • training data/label strategy
  • evaluation gates
  • scalable data pipelines
  • model packaging
  • CI/CD
  • serving reliability
  • observability/monitoring
  • incident/rollback readiness
  • cost/latency performance
  • high-governance environment
  • documentation, traceability, approvals, and audit readiness

Other signals

  • domain-specific foundation model
  • trained on structured payments data
  • prediction and classification problems
  • end-to-end execution across Applied AI/ML, engineering, data/feature pipelines, and governance/controls
  • downstream adoption