• Collaborate with team members to design, develop, and implement AI/ML models for applications such as predictive maintenance, anomaly detection, optimization problems, or natural language processing. • Analyze large datasets from various sources to extract insights and validate model performance. • Develop scripts or tools to automate data preprocessing, feature engineering, or model evaluation workflows. • Document methodologies and results clearly for internal presentations or reports.

• 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.

• Collaborates and pairs with other product team members (UX, engineering, and product management) to create secure, reliable, scalable machine learning solutions • Works with Product Team to ensure user stories that are developer-ready, easy to understand, and testable • Configures commercial off the shelf solutions to align with evolving business needs • Creates meaningful dashboards, logging, alerting, and responses to ensure that issues are captured and addressed proactively • Participates in learning activities around modern software design, machine learning, and development core practices (communities of practice) • Proactively views articles, tutorials, and videos to learn about new technologies and best practices being used within other technology organizations • Attends conferences and learns how to apply new innovations and technologies where appropriate • Researches and analyzes business trends and behavioral data to identify opportunities for improvement and new initiatives • Leads the evaluation development and recommendation of specific technology products and platforms to provide cost-effective solutions that meet business and technology requirements • Researches and designs best fit infrastructure, network, database, security, and machine learning architectures for products • Proactively creates and maintains tools for monitoring and support • Participates in project planning and management across multiple efforts • Develops formal training courses • Fields questions from other product teams or support teams • Monitors tools and participates in conversations to encourage collaboration across product teams • Provides application support for software running in production • Proactively monitors production Service Level Objectives for products • Proactively reviews the Performance and Capacity of all aspects of production: code, infrastructure, data, message processing, and prediction quality

• Collaborates and pairs with other product team members (UX, engineering, and product management) to create secure, reliable, scalable machine learning solutions • Documents, reviews, and ensures that all quality and change control standards are met • Works with Product Team to ensure user stories that are developer-ready, easy to understand, and testable • Writes custom code or scripts to automate infrastructure, monitoring services, and test cases • Writes custom code or scripts to do "destructive testing" to ensure adequate resiliency in production • Program configuration/modification and setup activities on large projects using HD approved methodology • Configures commercial off the shelf solutions to align with evolving business needs • Creates meaningful dashboards, logging, alerting, and responses to ensure that issues are captured and addressed proactively • Participates in learning activities around modern software design, machine learning, and development core practices • Fields questions from other product teams or support teams • Monitors tools and participates in conversations to encourage collaboration across product teams • Provides application support for software running in production • Proactively monitors production Service Level Objectives for products • Proactively reviews the Performance and Capacity of all aspects of production: code, infrastructure, data, message processing, and prediction quality