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As a Senior Director of Software Engineering at JPMorganChase within the Connected Commerce Technology organization, you lead multiple technical areas, manage the activities of multiple departments, and collaborate across technical domains. Your expertise is applied cross-functionally to drive the adoption and implementation of technical methods within various teams and aid the firm in remaining at the forefront of industry trends, best practices, and technological advances.
- Leads multiple technology and process implementations across departments to achieve firmwide technology objectives
- Directly manages multiple areas with strategic transactional focus
- Sets and scales multi-department strategy for agentic AI-enabled engineering and SDLC/TLM automation (using enterprise-authorized tools within the work environment) to drive firmwide objectives (speed, scalability, reliability, and cost-to-serve), including portfolio-level standards for AI-orchestrated delivery workflows, release governance, automated test modernization, resilience engineering, and incident response acceleration; establishes guardrails for validation, security, resiliency, traceability, and reuse.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to drive cross-domain reuse and measurable capacity unlock outcomes across departments.
- Provides leadership and high-level direction to teams while frequently overseeing employee populations across multiple platforms, divisions, and lines of business
- Acts as the primary interface with senior leaders, stakeholders, and executives, driving consensus across competing objectives
- Manages multiple stakeholders, complex projects, and large cross-product collaborations
- Influences peer leaders and senior stakeholders across the business, product, and technology teams
- Formal training or certification on software engineering concepts and 10+ years applied experience. In addition, 5+ years of experience leading technologists to manage, anticipate and solve complex technical items within your domain of expertise
- Experience designing and scaling a cross-channel biometric platform (not point solutions), including common APIs/shared policy/shared telemetry with modality-specific adapters (voice/face/iris/palm), integration across IVR/telephony, mobile SDK + camera pipelines, and kiosk/branch/partner flows, with strong reliability/latency discipline (especially for IVR voice) and graceful fallback for poor capture quality.
- Fluency in biometric ops and performance measurement (e.g., FAR/FRR, EER, TAR@FAR, FTE/FTA, drop-off, IVR AHT), with experience running A/B tests and tuning policies (step-up thresholds, risk-based routing) while protecting CX, plus a mature operating model for drift monitoring, incident response, vendor SLAs, and continuous evaluation.
- Experience defining and implementing presentation attack detection and an adversarial threat model with a fraud-first mindset across modalities, including voice replay/deepfake resistance and call audio injection risks (noise and device/line variability), face liveness and deepfake/face-swap and injection defenses (with device integrity signals), and iris/palm considerations (sensor trust, presentation attacks, and deployment constraints such as specialized hardware and environmental requirements).
- Knowledge of responsible biometrics practices, including bias/fairness evaluation and mitigation across demographics/environments (capture UX, thresholds, alternative paths), accessibility requirements for customers unable to use a modality, and privacy/regulatory readiness for biometric data (e.g., retention controls and purpose limitation).
- Experience engineering end-to-end identity lifecycle for biometrics—secure enrollment (proofing strength, step-up, dedupe, re-enrollment rules), strong binding to the correct account/device/session with change handling (new device, biometric drift/injury/aging), secure recovery that avoids lockouts, and clear consent/transparency with data-minimization practices.
- Demonstrated prior experience influencing across highly matrixed, complex organizations and delivering value at scale
- Experience leading multi-organization adoption of agentic AI-enabled engineering operating models (using enterprise-authorized tools within the work environment), including defining governance (human-in-the-loop decisioning, quality gates), measurement frameworks, and secure handling of sensitive inputs/outputs across teams.
- Deep understanding of responsible AI risk, controls, and resiliency/security expectations at scale, with demonstrated ability to advise senior leaders on safe adoption, portfolio governance, and reuse-first strategies.
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Experience leading complex projects supporting system design, testing, and operational stability