Scalable LLM Systems
Published in Red Hat, 2026
- Developed scalable infrastructure for high-throughput LLM inference and deployment.
- Optimized model serving pipelines using efficient batching, token scheduling, and inference optimization techniques.
- Designed architecture capable of supporting multiple concurrent users and high request volumes.
- Integrated vector databases, retrieval pipelines, and LLM inference components into end-to-end AI applications.
- Built monitoring and evaluation pipelines to ensure performance, scalability, and reliability of production LLM systems.