Staff Software Engineer, Fraud & Payment Risk Platform

Flex

4d•$170,000 - $250,000•Hybrid

About The Position

Flex is seeking a Staff Software Engineer to lead the architecture and hands-on development of backend services and shared infrastructure for its Fraud and Payment Risk Platform. The platform combines real-time, near-real-time, and offline or batch-computed risk features with business rules, machine-learning model outputs, and experimentation. The role is responsible for making these capabilities reliable, scalable, and observable so teams can develop, validate, and launch risk strategies safely and efficiently. This is a hands-on backend and platform position with technical ownership from architecture through production. The engineer will collaborate with product, risk policy, data science, and engineering teams to turn risk strategies into reusable platform capabilities. The scope includes feature ingestion and integration, user acceptance testing, experimentation, and engineering infrastructure across the machine-learning lifecycle—from development and validation workflows through deployment, serving, and production monitoring—in partnership with data scientists who own model research and training. The backend stack includes Java, Spring Boot, Python, and AWS.

Requirements

At least 6 years of professional software engineering experience, including substantial experience building and operating backend systems in production.
At least 5 years of hands-on Java experience, including building services with Spring Boot.
Experience leading the architecture and delivery of complex distributed systems or shared backend platforms.
Strong understanding of API design, service integration, data modeling, caching, and distributed-system failure modes.
Experience diagnosing performance bottlenecks and improving scalability and reliability through measurement, testing, and production troubleshooting.
Experience with cloud infrastructure, containerized deployments, and relational and NoSQL data stores.
Experience establishing observability, monitoring, and alerting for production systems.
Ability to lead technical decisions across teams, communicate architectural trade-offs clearly, and mentor other engineers.

Nice To Haves

Experience building risk platforms, decision engines, or fraud/payment risk systems; risk-domain experience is not required.
Experience with feature platforms, configurable rules engines, experimentation infrastructure, or production model-serving systems.
Experience building shared platforms that other engineering or risk teams adopt and operate independently.
Familiarity with AWS services such as EKS, Aurora, ElastiCache, and DynamoDB.
Experience with infrastructure as code and CI/CD automation using tools such as Terraform, CDK, and GitHub Actions.
Familiarity with observability tools such as Datadog and analytics platforms such as Snowflake.

Responsibilities

Lead backend architecture and development for the Fraud and Payment Risk Platform, including decisioning services, APIs, and shared infrastructure.
Design for scalability, performance, and reliability while balancing latency, throughput, availability, data consistency, and cost.
Build feature ingestion, integration, and serving capabilities for real-time, near-real-time, and offline or batch-computed features, with attention to freshness, correctness, and availability.
Develop infrastructure for rules and experimentation so teams can configure and version risk strategies, perform user acceptance testing, evaluate changes, and roll them out safely.
Own engineering infrastructure across the machine-learning lifecycle, supporting development and validation workflows, deployment, inference, versioning, safe rollouts, rollback, and integration of model outputs into risk decisions.
Own monitoring and alerting across the platform, covering service health, feature freshness and quality, rule and experiment execution, and model inference; partner with risk policy and data science teams to monitor model behavior and decision outcomes.
Improve platform extensibility and usability so teams can add features, rules, models, and experiments without duplicating infrastructure or tightly coupling systems.
Drive complex initiatives from design through production, including technical design proposals, implementation, performance testing, migration planning, and cross-team coordination.
Raise the engineering bar through design and code reviews, mentorship, and improvements to testing, documentation, deployment safety, and operations.