Senior Full Stack AI Engineer
Summary
Build AI-driven features (recommendations, fraud detection, automation) and lead a team to ship scalable full-stack web apps for a game studio.
- Lead the game development team by establishing coding standards, project architecture, and best practices.
- Design clean, modular, and maintainable code that can be easily extended for future game features.
- Review team members’ code and provide technical guidance and mentorship.
- Research and perform Proof of Concepts (POC) on new technologies and development approaches.
- Study competitors’ implementations and propose improvements to enhance game performance and user experience.
- Implement modern web technologies such as Service Workers, caching strategies, asset optimization, and fast-loading mechanisms.
- Collaborate closely with Game Designers, UI/UX Designers, Artists, Backend Developers, and QA throughout the development lifecycle.
- Participate in technical planning, estimation, and sprint discussions.
Requirements
- Design, develop, and deploy AI-powered solutions for user behaviour analysis, personalization, fraud detection, customer support automation, and operational intelligence.
- Build AI agents, workflow automation, recommendation systems, and predictive analytics models.
- Evaluate emerging AI technologies and recommend suitable solutions for business needs.
Full Stack Development
- Develop scalable web applications, APIs, and microservices using modern frameworks and technologies.
- Design and implement responsive frontend applications with excellent user experience.
- Build backend services, integrations, and data processing pipelines.
- Collaborate with Product, Design, Data, and Engineering teams throughout the software development lifecycle.
Data & Analytics
- Design data models, ETL processes, and real-time analytics pipelines.
- Work with large datasets to generate business insights and operational reporting.
- Develop dashboards and monitoring tools for system.
Platform & DevOps
- Implement CI/CD pipelines and cloud-native deployment strategies.
- Optimize application performance, scalability, reliability, and security.
- Monitor and troubleshoot production systems and AI services.