Senior Director of Software Engineering - CI&A Biometric & Externalization

Your opportunity to make a real impact and shape the future of financial services is waiting for you. Let's push the boundaries of what's possible together.

Job Responsibilities

• 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
  

Required qualifications, capabilities, and skills

• 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, , 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.

• Experience leading complex projects supporting system design, testing, and operational stability

Preferred qualifications, capabilities, and skills