Principal Solution Architect

Nice to Have Skills: Experience with Kubernetes/container orchestration, cloud platforms (AWS/Azure/GCP), 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. Roles and responsibilities: 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.

Title: Principal Solution Architect
Location: Fort Worth, TX 76155 (3 days hybrid onsite)
Duration: 6 months Contract

Job Overview:
As Sr Eng 2 Architect on the Agentic System Layer (ASL) team, you will define and drive the technical architecture for agentic AI platform.

Top 3 Mandatory Skills and Experience:

• 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).
• 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
• 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.

Roles and responsibilities:

• 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.

Nice to Have Skills:

• Experience with Kubernetes/container orchestration, cloud platforms (AWS/Azure/GCP), 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.
• Describe a great candidate that you are looking for and what skills and experience they will have: 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.