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We provide critical safety and performance layers for next-generation LLM applications, ensuring seamless integration of AI into global business ecosystems. We focus on building the "operating system" for AI services—solving high-concurrency challenges, optimizing distributed architectures, and setting the gold standard for service reliability at a global scale.
Job Responsibility:
Design and refine the end-to-end service operating model for high-traffic AI ecosystems
Define system behaviors under extreme load, implementing advanced failure handling, timeouts, and degradation strategies
Lead technical initiatives to strengthen the AI and API layers, eliminating structural single points of failure
Coordinate cross-component decisions and translate complex design patterns into reusable frameworks and high-quality documentation
Drive hands-on implementation while influencing technical direction across various engineering modules
Requirements:
7+ years in backend or platform engineering with a proven track record in designing scalable, high-traffic services
Extensive experience with Distributed Systems, Kubernetes, and cloud-native deployments
Strong command of performance tuning, service reliability engineering, and system-level trade-offs
Exceptional ability to lead technical discussions, document architectural decisions, and navigate complex design landscapes
Fluent in English with the ability to communicate technical trade-offs effectively in a cross-regional, multicultural environment
Experience with ML/AI-powered services or backgrounds in internal shared-service teams