Senior Software Engineer

Overview As a Senior Software Engineer in the Digital Banking Fraud team, you design and operate real-time, cloud-native fraud prevention systems at scale to protect trillions of dollars in transactions annually. You will modernize existing Python services, shape AI-driven development, and work across cross-functional partners to deliver reliable, low-latency capabilities. The role offers impact through improving platform reliability, observability, and engineering practices in a large enterprise environment. You will help steer how AI-assisted engineering is adopted in practice, driving faster, higher-quality software delivery. Compensation / BenefitsHybrid Work OpportunitiesFlexible Time OffCareer Development & Mentoring ProgramsHealth & Wellness Benefits (including health insurance)Parental Leave for eligible new parentsEmployee Peer Recognition Programs – “You Earned it” ResponsibilitiesDesign and operate reliable, low-latency fraud prevention services and APIsDeliver new fraud capabilities and improve production systemsModernize Python services, shared patterns, tooling, and practicesAdvance AI-assisted and agentic software engineering approachesDesign and operate cloud-native AWS architectures (serverless, event-driven)Integrate with Q2’s enterprise banking platform and modify C#/.NET integration points as neededImprove observability, testing, deployment, and reliabilityCollaborate with data scientists, product teams, and engineers to bring fraud detection to productionResearch new technologies and apply them to improve the platformMentor engineers and establish maintainable patterns and strong engineering practices Key requirementsStrong experience building and operating production software systemsProficiency in Python and building maintainable services and APIsCloud architecture experience (AWS or equivalent)Infrastructure as code and modern CI/CD practicesDistributed, event-driven, or serverless design experienceAPI design, system integration, reliability, and observabilityAbility to work in large/complex enterprise codebasesWillingness to learn across technologiesGit-based workflows and modern Python toolingStrong analytical and problem-solving skillsEffective cross-functional communicationInterest in AI-powered and agentic development toolsEffective communicationCollaboration across engineering, data science, and product teamsProblem-solving mindsetPythonAWS (serverless, Lambda, DynamoDB, SQS, Kinesis, S3)Infrastructure as code (CloudFormation, SAM, Terraform, CDK)