• Design and ship production grade AI agents for insurance workflows where latency, observability and reliability are non-negotiable • Shape the architecture of a platform complex enough to challenge even the best engineers • Partner with product to ship secure, user friendly features that tackle complex models and calculations • Own the creation and maintenance of unit tests, using AI assisted testing to increase coverage and reduce defect rates • Solve performance, scalability, and reliability challenges that keep our systems running at peak efficiency • Work with a team that gets genuinely excited about what they're building and can't wait to share it • Actively track emerging AI tooling and bring concrete recommendations back to the team

• Contribute to software architecture design • Deliver tested features • Collaborate in scrum meetings and ticket grooming • Report directly to Product Owner

• Work in the backend architecture and services that support NBA 2K’s online features • Design and optimize distributed systems to ensure scalability, reliability, and fault tolerance • Build and maintain large-scale computing solutions that handle millions of concurrent players • Develop efficient database structures and queries to support high-volume, real-time transactions • Deliver high-performance backend services with a focus on low latency and stability • Partner with engineering and design teams to enable seamless online gameplay experiences.

• Develop and maintain Python-based applications and tools that support our AML solutions • Implement, optimize, and deploy machine learning models, including large language models, to enhance our product capabilities • Collaborate with data scientists and cross-functional teams to integrate machine learning models into production systems • Design and implement data integration, validation, and quality check frameworks • Work on the entire machine learning pipeline, from data preprocessing to model deployment and monitoring • Create quick prototypes and present them to customers and internal stakeholders to drive product development • Analyze and process large-scale structured and unstructured data sets to support machine learning initiatives • Ensure the robustness, scalability, and efficiency of the developed applications and models • Provide technical support and troubleshoot issues related to machine learning models and Python applications • Follow best practices in coding, MLOps, and DataOps • Assist other teams in delivering proof-of-concept projects