AI Native Principal Architect (Hybrid)
$133,109–$239,596 year
HybridScottsdale, Arizona, United States
Job Summary
Lead architecture design and implementation of cloud native solutions for identity fraud detection, risk analysis, and digital intelligence platforms on AWS. Establish coding standards, conduct architecture reviews, and mentor engineers while designing RESTful services that handle large volumes of real-time data with low latency. Architect systems using Java, Spring Boot, Docker, Kubernetes, and AWS services to build event-driven data-intensive platforms processing device signals and risk scoring at scale. Guide AI native engineering practices and embed AI and ML capabilities into product features including anomaly detection and device intelligence. Partner with product, engineering, and data science teams to translate fraud requirements into technical solutions and manage technical strategy across multiple engineering teams.
Required Qualifications
- 10+ years of experience designing cloud-native solutions on AWS
- expertise in AWS services (EC2, RDS, DynamoDB, Kinesis, Lambda)
- expertise in Docker/Kubernetes
- expertise in infrastructure-as-code
- Experience designing scalable, resilient, and cost-optimized Java architectures
- Experience architecting complex, mission-critical systems serving millions of requests
- Track record translating requirements into technical solutions while managing multiple engineering teams and partner dependencies
- Expertise in designing event-driven, data-intensive platforms and APIs
- Knowledge of asynchronous processing
- Knowledge of message brokers (Kafka)
- Knowledge of database optimization
- Knowledge of API design principles
- Knowledge of techniques for achieving low-latency, high-throughput systems
- Experience leading technical strategy
- Experience establishing architecture standards
- Experience mentoring engineers
- Experience translating technical concepts and aligning engineering with business objectives
- Hybrid work from our Scottsdale, AZ office is preferred; however, remote candidates will also be considered
Desired Qualifications
- Experience architecting 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
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