Sr. 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.