Principal Solution Architect (ASL)

Principal Solution Architect- Fort Worth, TX  - Must Need Near To Fort Worth (50-M) & Onsite Interview )   15+ Years of Experience As Sr Eng 2 Architect on the Agentic System Layer (ASL) team, you will define and drive the technical architecture for American Airlines’ agentic AI platform. Day-to-day responsibilities include: designing and evolving the architecture for multi-agent orchestration systems, tool-use frameworks, and LLM integration pipelines; establishing patterns for agent reliability, observability, and guardrails at production scale; leading technical design reviews and producing architecture decision records (ADRs); collaborating with ML engineers and software engineers to ensure platform components are scalable, secure, and maintainable; evaluating and integrating emerging agentic AI frameworks (e.g., LangGraph, CrewAI, Semantic Kernel, AutoGen); defining API contracts, data flow patterns, and integration standards across the AI platform ecosystem; mentoring engineers on best practices for building production-grade AI systems. Top 3 Mandatory Skills and Experience: 1) 10+ years software architecture experience with at least 3 years designing AI/ML platform systems, including hands-on experience with LLM orchestration frameworks (LangChain, LangGraph, Semantic Kernel, or similar). 2) Deep expertise in distributed systems design, microservices architecture, event-driven patterns, and API design (REST/gRPC), with strong proficiency in Python and at least one of Java/Go/TypeScript. 3) Production experience building and deploying agentic AI systems or LLM-powered applications at scale, including prompt engineering, tool-use patterns, RAG pipelines, and agent reliability/observability. Nice to Have Skills: Experience with Kubernetes/container orchestration, cloud platforms (AWS/Azure/Google Cloud Platform), MLOps/LLMOps tooling, vector databases (Pinecone, Weaviate, pgvector), knowledge graphs, airline/travel domain experience, TOGAF or similar architecture certification, experience with multi-agent system design patterns and agent evaluation/benchmarking frameworks. What Makes a Great Candidate?: A great candidate is a seasoned architect who has shipped production agentic AI systems - not just prototypes. They can whiteboard a multi-agent orchestration system, debate tradeoffs between different LLM routing strategies, and then jump into code to prove out a design. They understand that agentic systems at airline scale need bulletproof reliability, graceful degradation, and real observability. They have opinions backed by experience, they push back on bad ideas constructively, and they make the engineers around them better. What is the team environment and structure like?: The AI Platforms Capabilities team builds and operates the Agentic System Layer (ASL), which is American Airlines’ core platform for deploying agentic AI systems. The team operates in an agile environment with a focus on rapid iteration, production reliability, and close collaboration between architects, engineers, and ML engineers. The team works hybrid from the DFW office.   The resource will be embedded directly into the ASL squad, working alongside full-time engineers and architects on platform development, feature delivery, and production support. They will participate in sprint ceremonies, code reviews, and architecture discussions. Comment: Candidate will be working hybrid; onsite 3 days a week and 2 days virtually. Open to H1B candidates. Interviews will have 2 rounds. Top 3 Mandatory Skills and Experience: 1) 10+ years software architecture experience with at least 3 years designing AI/ML platform systems, including hands-on experience with LLM orchestration frameworks (LangChain, LangGraph, Semantic Kernel, or similar). 2) Deep expertise in distributed systems design, microservices architecture, event-driven patterns, and API design (REST/gRPC), with strong proficiency in Python and at least one of Java/Go/TypeScript. 3) Production experience building and deploying agentic AI systems or LLM-powered applications at scale, including prompt engineering, tool-use patterns, RAG pipelines, and agent reliability/observability. Nice to Have Skills: Experience with Kubernetes/container orchestration, cloud platforms (AWS/Azure/Google Cloud Platform), MLOps/LLMOps tooling, vector databases (Pinecone, Weaviate, pgvector), knowledge graphs, airline/travel domain experience, TOGAF or similar architecture certification, experience with multi-agent system design patterns and agent evaluation/benchmarking frameworks."