Senior Software Engineer

Job DetailsJob Location: Fort Collins, CO 80528Position Type: Full TimeSalary Range: $132,800.00 - $185,000.00 SalaryTravel Percentage: NegligibleJob Shift: DayPOSITION SUMMARY The Software Engineer designs and builds infrastructure and applications for BillGO’s products. This role will be collaborating internally with teammates and other teams as well as externally with third party vendors through a variety of transmission protocols. This role will be fully responsible for code through design, implementation, maintenance, and monitoring. The ideal candidate will have a strong background in Java development, Cloud architecture, and experience in the financial industry. About the Role As our Issuing Engineer, you will: Design, build, and maintain the core systems behind our virtual card issuing platform Integrate AI-powered services for fraud detection, anomaly monitoring, and transaction insights Build APIs and services that power card creation, spend controls, tokenization, and reconciliations Collaborate with Product, Risk, and AI/ML teams to develop machine learning–driven authorization rules and customer-facing analytics Work closely with network partners (Visa, Mastercard) and issuing processors to ensure secure, compliant, and performant issuing flows Act as a subject matter expert on issuing technology and AI-driven enhancements What You’ll Do Lead technical development of issuing features: card lifecycle management, spend controls, tokenization, and real-time transaction processing Own integrations with card networks and issuing processors (Marqeta, Galileo, Lithic, or direct to network) Build resilient, observable systems that handle real-time authorization events and sensitive payment data Implement real-time AI-based fraud detection pipelines that learn from transaction patterns Integrate LLM-powered support and operations tooling for internal teams (e.g., automated dispute summaries, transaction anomaly explanations) Build AI-driven customer dashboards showing spend patterns, merchant risk profiles, and cost optimization recommendations Participate in architecture reviews, incident response, and production hardening Monitor and optimize system performance and cost-efficiency as the platform scales Stay current on card network updates, fraud trends, AI advancements, and regulatory shifts QualificationsYou May Be a Good Fit If You Have 5+ years backend engineering experience, ideally in FinTech or Payments Have 2+ years working directly on card issuing, processing, or payment systems Are comfortable with card networks, BIN sponsors, issuing processors Understand authorization flows, ISO8583, PCI-DSS, and tokenization Are fluent in backend development (Node.js, Python, Go, or Java) and distributed system design Have hands-on experience integrating AI/ML models into production systems Can design secure, auditable, and highly-available services Are product-minded and love influencing roadmaps with data-driven and AI-enabled insights Thrive in startup environments with evolving needs Nice to have Experience with Marqeta, Galileo, Lithic, or other issuing platforms Experience with fraud detection models, merchant categorization ML, or real-time anomaly detection Familiar with vector databases and LLM orchestration frameworks (LangChain, Semantic Kernel, etc.) Built customer-facing AI features such as transaction categorization, forecasting, or spend optimization Exposure to PCI-DSS, SOC 2, KYC/AML, and compliance-heavy environments What Success Looks Like in This Role  Modernize the issuing platform to support 3–5x× transaction volume growth while improving authorization latency, resiliency, and operational visibility.  Reduce infrastructure and processing costs through architectural improvements, smarter routing, and data-driven performance optimization.  Improve partner onboarding velocity with card networks and processors by standardizing integrations and reducing custom operational overhead.  Deliver actionable insights via dashboards and analytics that help customers optimize spend, manage risk, and reduce costs.  Continuously adapt authorization logic using ML-driven insights from live transaction data, merchant behavior, and customer usage patterns.