Технологии: AI Feedback, Context engineering, Data Curation, Deep learning, DeepSpeed, Direct Preference Optimization, Distributed Training, Evaluation, FSDP, Fine Tuning, Human Feedback, Inference Optimization, KV cache, Knowledge Distillation, Kv cache reuse, LLM-as-a-Judge, Language Models, Large Language Models, Learning from Human Feedback, Long Context, Long Context Training, Low Latency, Low-latency serving, Machine Learning, Megatron, Memory modeling, Mixture of Experts, Model Serving, Multi-tenant, Multi-tenant systems, Observability, Online Reinforcement Learning, Parallel Tool Use, Preference Data Curation, Preference data, Preference optimization, Privacy and Quality Gates, Prompt Caching, Quality gates, Quantization aware training, Regression testing, Reinforcement Learning, Reinforcement Learning from AI Feedback, Reinforcement Learning from Human Feedback, Retrieval Indexing, Retrieval-Augmented Generation, Reward Modeling, Safety evaluation, Speculative decoding, Supervised Fine Tuning, TensorRT-LLM, Tool design, Tool use, Tracing, VLLM