Revenue Systems Engineer
FrankieOne is a RegTech company that provides a unified connection to KYC, KYB, AML, and fraud tools. Its platform helps banks, fintechs, and financial-services companies onboard customers, manage risk, and monitor transactions.
About FrankieOne
FrankieOne provides a unified API, customizable decision engine, and single customer view for customer onboarding, identity and business verification, AML, fraud protection, biometrics, risk-based onboarding, and transaction monitoring. The company connects customers to hundreds of global vendors and data sources, enabling organizations to configure and activate checks and verifications with minimal development work. Its customers include banks, fintechs, and other highly regulated financial-services companies.
Skills
About the Role
You will take end-to-end ownership of production systems supporting revenue operations. You will build and operate AI and machine-learning systems, high-throughput data pipelines, backend APIs, automation infrastructure, internal tools, dashboards, and reporting services. You will monitor system performance and data quality, respond to incidents, document systems and data models, establish engineering standards, and translate business needs into production engineering solutions.
Responsibilities
- Design, build, and maintain end-to-end machine-learning systems, including training pipelines, model serving, and API deployment.
- Develop and operate AI-powered content generation and analysis systems.
- Build evaluation pipelines, feedback loops, and regression monitoring frameworks.
- Own the architecture, deployment, performance monitoring, and continuous improvement of deployed AI systems.
- Identify automation opportunities and design AI-first solutions.
- Design and operate high-throughput batch and real-time data pipelines.
- Build and maintain backend APIs and processing services integrating HubSpot, Xero, Redshift, and other revenue systems.
- Architect scalable, low-latency data infrastructure.
- Develop commission calculation engines, financial reconciliation systems, and contract data extraction pipelines.
- Own data-quality monitoring, alerting, and incident response.
- Build internal tools and dashboards using React and Python.
- Develop email processing, workflow classification, and automation APIs.
- Create reporting and analytics services for enterprise clients and account managers.
- Build training-data systems and evaluation infrastructure.
- Maintain and improve production RevOps tools for reliability and performance.
- Take technical ownership from architecture and scoping through deployment and ongoing operation.
- Document systems, APIs, and data models.
- Establish engineering practices for code review, testing, monitoring, and deployment.
- Translate business needs into engineering specifications.
- Mentor the RevOps Manager in Python, automation, and AI tooling.
