Lead Architect, Multi-Agent AI Platform

Quantum Technologies. LLC; Fort Worth (TX); On-site; USD 180,000 - 240,000; Full time

Job summary

Quantum Technologies. LLC in Fort Worth, TX seeks a Sr Eng 2 Architect to define and drive the technical architecture for the agentic AI platform.

You will lead multi-agent orchestration, tool-use frameworks, and LLM integration pipelines, ensuring scalability, security, and maintainability. You will mentor engineers, participate in ADRs, and collaborate with ML and software teams to establish patterns for reliability, observability, and guardrails at production scale.

Qualifications

• 10+ years software architecture experience with at least 3 years designing AI/ML platform systems.
• Hands-on experience with LLM orchestration frameworks (LangChain, LangGraph, Semantic Kernel, or similar).
• Deep expertise in distributed systems design, microservices, event-driven patterns, and API design (REST/gRPC).
• Proficiency in Python and at least one of Java/Go/TypeScript.
• Production experience building agentic AI systems or LLM-powered applications at scale, including prompt engineering, tool-use patterns, RAG pipelines, and agent reliability/observability.

Responsibilities

• Define and drive the technical architecture for multi-agent orchestration systems and LLM integration pipelines.
• Lead architecture reviews and produce ADRs, ensuring scalability, security and maintainability.
• Collaborate with ML engineers and software engineers to ensure platform components are scalable and observable.

Skills

Software architecture

AI/ML platform design

Distributed systems

Tools

LangChain

LangGraph

Semantic Kernel

REST/ gRPC

Job description

Quantum Technologies. LLC in Fort Worth, TX seeks a Sr Eng 2 Architect to define and drive the technical architecture for the agentic AI platform.

You will lead multi-agent orchestration, tool-use frameworks, and LLM integration pipelines, ensuring scalability, security, and maintainability. You will mentor engineers, participate in ADRs, and collaborate with ML and software teams to establish patterns for reliability, observability, and guardrails at production scale.