Fort Worth, TX30+ days ago
Day-to-day responsibilities include: developing and optimizing LLM-powered agent pipelines including prompt engineering, chain-of-thought reasoning, and tool-use patterns; building RAG (Retrieval Augmented Generation) systems with vector search, embedding models, and knowledge retrieval pipelines; implementing agent evaluation, benchmarking, and regression testing frameworks; fine-tuning and optimizing model inference for latency and cost (quantization, caching, batching, model routing); developing guardrails, content filtering, and safety mechanisms for production agent deployments; collaborating with software engineers on model serving infrastructure and with architects on system design; staying current with rapid advances in agentic AI, LLM capabilities, and evaluation methodologies. Nice to Have Skills: Experience with model fine-tuning (LoRA, QLoRA), model serving (vLLM, TGI, Triton), multi-agent orchestration frameworks, reinforcement learning from human feedback (RLHF), MLOps/LLMOps platforms, knowledge graph construction, cost optimization for LLM inference, airline or travel domain experience.