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Director, Fraud Hub Lead Engineer

Job in New York, New York County, New York, 10261, USA
Listing for: BNY
Full Time position
Listed on 2026-06-03
Job specializations:
  • Engineering
    Cybersecurity, AI Engineer
Salary/Wage Range or Industry Benchmark: 100000 - 125000 USD Yearly USD 100000.00 125000.00 YEAR
Job Description & How to Apply Below
Location: New York

Fraud Hub Lead Engineer

We’re seeking a future team member for the role of Fraud Hub Lead Engineer to join our Corporate Engineering team. This role can be in NYC, NY.

Role Overview

We are seeking an experienced Fraud Hub Lead Engineer to lead the global Engineering strategy and execution for BNY’s Fraud Hub. This role serves as the firmwide engineering authority for Fraud detection, prevention, and response platforms, with responsibility for defining architecture, driving AI‑first solutions, and delivering secure, resilient, and scalable systems.

As part of Corporate Engineering, you will lead the engineering organization responsible for the platforms that protect identity and payment methods across wires, cheques, cards, and emerging payment channels. This is a senior leadership role for a hands‑on, technically credible engineering leader who combines deep Fraud domain expertise with modern software, data, and AI engineering capabilities.

Key Responsibilities
  • Define and execute the global engineering strategy for BNY’s Fraud Hub, acting as the single engineering owner for Fraud platforms, standards, and delivery.
  • Build, lead, and inspire a high‑performing global engineering organization with strong technical depth, clear ownership, and a relentless focus on quality and outcomes.
  • Establish engineering standards for availability, resiliency, security, model risk management, and operational excellence across all Fraud technology.
  • Own the end‑to‑end architecture, build, and operation of scalable, real‑time Fraud detection and decisioning platforms.
  • Design and operate streaming, event‑driven systems for real‑time Fraud detection, monitoring, and response across high‑volume payment and transaction flows.
  • Integrate emerging AI‑driven systems for anomaly detection, behavior analysis, alert prioritization, investigation, and remediation, including biometrics, video and voice analytics.
  • Develop retrospective and “after‑event” analytics capabilities to continuously improve models, controls, and defenses against emerging Fraud threats.
  • Anticipate new Fraud vectors and proactively evolve platforms, data, and AI capabilities using a 360 mindset to stay ahead of the threat landscape.
  • Provide full lifecycle ownership for Fraud platforms, including build, run, resiliency, incident response, and continuous improvement.
  • Establish, track, and report KPIs and OKRs including uptime, MTTR, change success rate, SLA adherence, throughput, and operational risk indicators.
  • Champion modern Dev Sec Ops  and MLOps practices, ensuring safe, repeatable, and auditable deployment of software and AI into production.
  • Partner closely with Risk, Compliance, Cyber, Legal, Operations, and Product teams to translate regulatory and risk requirements into actionable engineering outcomes.
  • Serve as a trusted engineering leader to senior executives and stakeholders, influencing investment, prioritization, and strategic direction.
  • Provide a firmwide hub of Fraud engineering expertise, intelligence, and best practices, enabling consistent, high‑quality outcomes across the enterprise.
Required Qualifications & Experience
  • 10+ years of software engineering experience, with at least 5 years delivering Fraud, Financial Crime, or large‑scale risk analytics platforms.
  • Proven experience building and deploying AI/ML‑driven solutions for Fraud detection, anomaly detection, or real‑time risk decisioning.
  • Demonstrated success leading and scaling global engineering teams responsible for mission‑critical, regulated technology platforms.
  • Hands‑on technical credibility and use of emerging AI tools, able to design systems, review code, evaluate architectures, and build working prototypes when needed.
  • Experience operating AI in production environments, including model lifecycle management, monitoring, drift detection, explainability, and governance.
  • Strong understanding of modern distributed systems, event‑driven architectures, streaming platforms, and cloud‑native design patterns.
  • Familiarity with cybersecurity practices, identity and access management, logging/SIEM integrations, and secure‑by‑design engineering.
Preferred Qualifications
  • Experience working with industry Fraud…
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