• Define, own, and execute the long‑term AI and Machine Learning strategy for the Documents & Biometrics domain, aligned with company objectives and product roadmaps. • Identify opportunities where machine learning can materially improve classification, extraction, fraud detection, image processing, and overall product performance. • Serve as a thought leader for AI/ML within the organization, advocating for modern approaches, emerging technologies, and best practices. • Provide hands‑on technical leadership across the full ML lifecycle, including research, model design, experimentation, validation, deployment, and continuous improvement. • Raise the bar for technical excellence while fostering an inclusive, high‑engagement team culture. • Oversee the development and productization of ML models addressing real‑world document and biometric challenges at scale. • Establish and evolve robust MLOps practices to ensure reproducibility, reliability, observability, cost effectiveness, and consistent high‑quality model delivery. • Ensure the availability, quality, and scalability of labeled data pipelines necessary to support ongoing model development and accuracy improvement. • Lead, mentor, and develop a team of senior machine learning engineers and technical leaders, fostering a culture of trust, accountability, collaboration, and continuous learning. • Build high‑performing teams that balance innovation with operational excellence. • Set clear expectations, provide regular feedback, and support the professional growth and progression of team members. • Partner closely with Product Management to define AI/ML roadmaps, prioritize initiatives, and ensure timely and high‑impact delivery. • Collaborate effectively with Engineering, Architecture, Data, Platform, Security, Legal, and Compliance teams to ensure ML systems are scalable, secure, and compliant. • Represent Documents & Biometrics in cross‑company forums related to AI strategy, governance, and innovation. • Ensure that machine learning systems are developed and operated in accordance with applicable AI governance frameworks, regulatory requirements, and ethical best practices. • Contribute to company‑wide AI governance efforts, including AI risk assessment, documentation, explainability, and stakeholder readiness. • Manage multiple complex initiatives simultaneously, balancing innovation, delivery commitments, and operational stability. • Ensure adherence to industry best practices, architectural standards, and engineering quality bars. • Maintain high levels of team morale, engagement, and delivery velocity.