As the AI Native Principal Architect, you will help define the future of our platform by balancing long-term architectural vision with practical execution. Working with engineering, product, and business leaders, you'll promote modern engineering practices, foster technical growth across teams, and ensure our technology foundation continues to evolve with the business.You will report to the Director of Engineering. Hybrid work from our Scottsdale, AZ office is preferred; however, remote candidates will also be considered.You'll have the opportunity to:Lead architecture design and implementation of cloud native solutions for identity fraud detection, risk analysis, and digital intelligence platforms on AWS.Ensure solutions are scalable, resilient, secure, and efficient while maintaining agreement on goals.Make architecture decisions and manage technical strategy for the digital intelligence and device fingerprinting domains.Establish coding standards, conduct architecture reviews, and mentor engineers on best practices.Design and implement RESTful services that can handle large volumes of real-time data with low latency and high availability.Architect systems using Java, Spring Boot, Docker, Kubernetes, AWS services, Apache, Kafka, and NoSQL and SQL databases.Provide technical leadership across engineering teams on solution design, architecture patterns, and technology choices.Partner with product, engineering, data science, security, and strategic partners to translate fraud and risk requirements into technical solutions.Build event-driven data-intensive platforms processing device signals, behavioral data, fraud indicators, and risk scoring at scale.Architect systems supporting continuous delivery, observability, security, compliance, and cost optimization for operational efficiency.Guide AI native engineering practices and embed AI and ML capabilities into product features, including fraud detection, anomaly detection, risk scoring, and device intelligence.Promote AI tool adoption for development, productivity testing, and operational efficiency.
