As a Principal Technical Program Manager in our Commercial & Investment Bank, Payments Technology Team, you will drive delivery of the Large Payments Model (LPM)—a domain-specific foundation model trained on structured payments data (not language). LPM learns cross-cutting patterns from payments data and enables a single pretrained model to support multiple products, rails, and use cases.

This role leads end-to-end execution across Applied AI/ML (model development), engineering (serving and integration), data/feature pipelines, and governance/controls, ensuring LPM is delivered with technical excellence, scalability, and downstream adoption. LPM is optimized for prediction and classification problems central to payments (e.g., fraud detection, risk signaling, payment optimization)—not generative chat. You will translate business, technical, and operational objectives into an integrated program plan, managing resources, timelines, and budgets (as applicable) while navigating ambiguity and driving change.

Job responsibilities

• Own the program plan for LPM delivery: milestones, critical path, dependencies, resourcing, and operating cadence across multiple teams.
• Oversee execution across resources, timelines, and budgets (as applicable); manage dependencies and control change in high-pressure, shifting environments.
• 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.
• Coordinate the Science track: training data/label strategy, model objectives, evaluation gates, robustness/segmentation, calibration, explainability inputs (as required), and model release criteria.
• Coordinate the Production track: scalable data pipelines, model packaging, CI/CD, serving reliability, observability/monitoring, incident/rollback readiness, and cost/latency performance.
• Guide selection and implementation of appropriate platforms, tooling, and integration approaches to support LPM training/evaluation, serving, monitoring, and downstream adoption.
• Establish and run program mechanisms: sprint/program planning, cross-team standups, architecture reviews, risk reviews, release readiness, and executive updates.
• Manage RAID (risks, assumptions, issues, dependencies) proactively—especially around data readiness, integration timelines, platform constraints, and adoption readiness across products/rails.
• Partner with Risk/Compliance/Model Governance and controls to ensure documentation, traceability, approvals, and audit readiness are planned and delivered as part of the program.

Required qualifications, capabilities, and skills

• 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).

Preferred qualifications, capabilities, and skills

• 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.