Sr. ML Engineer - Fort Worth, TX (Hybrid) - Need 12+ Years Exp and Ready to Go Face - to - Face Interview

Job Title: Sr. Engineer Location: Fort Worth, TX Hybrid (3 days a week onsite & 2 days virtual) Duration: Long-term In-person Interview Description: • 10+ Years of Experience • As a Machine Learning Engineer on the Agentic System Layer (ASL) team, you will build the ML-powered components that make American Airlines’ agentic AI systems intelligent and reliable. 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 (quantisation, 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. Top 3 Mandatory Skills and Experience: • 10+ years in ML engineering or applied ML, with at least 3 years hands-on experience with LLMs (GPT-4, Claude, Llama, Mistral, or similar) • strong Python proficiency and experience with ML frameworks (PyTorch, HuggingFace Transformers). • Production experience building RAG systems, including vector databases (Pinecone, Weaviate, pgvector, FAISS) • Embedding models, chunking strategies, and retrieval optimization; experience with prompt engineering and chain-of-thought patterns. • Experience with ML evaluation and experimentation - building evaluation harnesses, A/B testing, regression testing for LLM outputs, and defining quality metrics for non-deterministic AI systems. 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.