AI & MLOps Engineer: Supercharging LLM Inference and RAG Pipelines!
infrastructure#llm📝 Blog|Analyzed: Feb 21, 2026 02:03•
Published: Feb 21, 2026 02:00
•1 min read
•r/mlopsAnalysis
This AI & MLOps Engineer is making waves in the field of Large Language Model (LLM) Inference and Retrieval-Augmented Generation (RAG). With impressive advancements in throughput, latency reduction, and cost optimization, this engineer is clearly at the forefront of AI infrastructure. Their expertise promises to significantly improve the efficiency and performance of cutting-edge AI applications.
Key Takeaways
- •Expert in optimizing LLM Inference for speed and efficiency.
- •Experienced in building and deploying scalable AI microservices on Kubernetes (EKS).
- •Proficient in various techniques to reduce latency and costs, including quantization.
Reference / Citation
View Original"Successfully increased throughput from 20 to 80 tokens/sec (4x) by migrating systems to vLLM with PagedAttention and Continuous Batching."