Description Overview At Fanatics Betting & Gaming (FBG), a core division of Fanatics' mission to establish the ultimate end-to-end digital sports platform, we're crafting a world where every aspect of a sports fan's passion is catered to. This Software Engineer III (Distributed Systems - Kotlin, Java, Kafka) role entrusts you with significant ownership over the development and optimization of real-time systems that power our sports betting platform. You'll lead by example, establishing best practices for AI-assisted development while building systems that handle millions of events daily. On the engineering side, we're pioneering the use of AI as a code collaborator. We need engineers who have gone beyond experimentation—who actively use Claude Code, Cursor, GitHub Copilot, or similar tools to ship production code faster while maintaining exceptional quality standards. If you have strong opinions about AI-augmented development workflows backed by real experience, we want to talk with you. Responsibilities Design, build, and optimize real-time betting systems handling 10K events per second Ensure 99.999% uptime for customer-facing services through robust error handling and failover strategies Optimize database queries, caching strategies, and event streaming pipelines for sub-100ms response times Full feature ownership: spec writing → implementation → deployment → monitoring → iteration based on metrics Self-motivated ability to have an idea, build it, and support it! Leverage AI tools to accelerate development velocity while maintaining code quality standards Establish and document team standards for AI tool usage (prompt patterns, code review checklists, validation strategies) Measure and report on AI tool ROI through concrete metrics (PR velocity, bug rates, test coverage) Identify and prevent common AI-generated code pitfalls (over-abstraction, missing edge cases, security vulnerabilities) Qualifications 7 years building and deploying scalable, high-performance production applications Kotlin and/or Java: 3 years building production microservices Spring Boot: Deep understanding of reactive programming and non-blocking I/O PostgreSQL: Complex query optimization, indexing strategies, and migration management Kafka: Event streaming patterns, partition strategies, and consumer group management at scale Redis/Redis Pub/Sub: Building real-time features supporting hundreds of thousands of concurrent users Demonstrated experience using AI tools (Claude Code, Cursor, Copilot, etc.) to ship production code Can articulate specific examples of workflow improvements (e.g., "reduced boilerplate generation time by 40%") Has developed personal strategies for validating AI-generated code and identifying common pitfalls Can compare at least 2-3 AI tools with concrete pros/cons from actual usage Strong grasp of software design principles (SOLID, DRY) and testing methodologies (TDD, BDD) Track record of introducing tools or processes that measurably improved team velocity Experience with observability and monitoring in distributed systems Can write clear technical documentation and present architecture decisions to non-technical stakeholders Team-first mentality with willingness to jump in wherever needed Actively experiments with and optimizes personal development workflow Strong written and verbal communication skills Self-directed problem solver who thrives in ambiguous situations Preferred Qualifications Experience in sports betting industry or genuine interest in sports Previous experience in high-growth startup environments Contributions to open-source projects or technical community Experience with real-money transaction systems and regulatory compliance Background in building developer tools or improving engineering productivity Experience leading without authority and influencing technical direction across teams Note on AI Workflow: We're serious about AI-augmented development. During interviews, be prepared to: Demo your current AI-assisted workflow Discuss specific examples of how you've used AI to solve complex problems Share your strategies for ensuring AI-generated code meets production standards