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AI Summary
Lead backend architecture and development for a Fraud and Payment Risk Platform at Flex, a NYC-based FinTech company. Responsibilities include designing scalable, reliable systems for real-time/near-real-time fraud prevention, integrating machine learning models, and enabling experimentation. Requires 6+ years of backend engineering experience, 5+ years in Java/Spring Boot, and expertise in distributed systems, observability, and cloud infrastructure.
Key Responsibilities
Lead backend architecture and development for Fraud and Payment Risk Platform
Design scalable, reliable systems for real-time/near-real-time fraud prevention
Integrate machine learning models and enable experimentation infrastructure
Develop infrastructure for rules, feature ingestion, and UAT workflows
Own ML lifecycle from development to production monitoring
Improve platform extensibility and usability for cross-team adoption
Drive complex initiatives from design to production deployment
Mentor engineers and raise the engineering bar through reviews and improvements
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Benefits & Perks
Market-based compensation with tiered pay ranges based on location
Hybrid work options (2-3 days on-site for NYC/SF/Salt Lake City roles)
Relocation assistance program for candidates outside primary locations
Diverse, inclusive workplace with global team presence
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Nice to Have
Experience with risk platforms, decision engines, or fraud/payment risk systems
Familiarity with feature platforms, configurable rules engines, or model-serving systems
Experience with AWS services (EKS, Aurora, ElastiCache, DynamoDB)
Infrastructure as code (Terraform, CDK) and CI/CD automation (GitHub Actions)
Tools like Datadog for observability and Snowflake for analytics
Job Description
Flex is looking for a Staff Software Engineer to build and scale their Fraud and Payment Risk Platform. You will lead the architecture and hands-on development of backend services and shared infrastructure for fraud prevention and payment risk decisioning.
The platform combines real-time, near-real-time, and offline/batch-computed risk features with business rules, machine learning model outputs, and experimentation. Your role involves making these capabilities reliable, scalable, and observable, enabling teams to develop, validate, and launch risk strategies faster and more safely.
Key responsibilities include:
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