Fanatics Betting & Gaming (FBG) is looking to develop and optimize real-time systems for their sports betting platform, handling millions of events daily and requiring high uptime and low latency. They are also pioneering the use of AI as a code collaborator to improve development velocity and quality.
Requirements
- 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
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
- 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)
Other
- Full feature ownership: spec writing implementation deployment monitoring
- iteration based on metrics
- Self-motivated ability to have an idea, build it, and support it!
- 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
