Sr. ML Engineer (Airline exp must)- Fort Worth, TX (Hybrid) - F2F 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 AAs 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.