AI Engineer – Agentic AI, RAG & LangChain Expert

Motion Recruitment; Fort Worth (TX); Hybrid; USD 96,000 - 193,000; Full time

Job summary

Motion Recruitment in Fort Worth, TX seeks an experienced AI Engineer to design, develop, and deploy scalable AI solutions in production. You will leverage Python and Agentic AI, building RAG-based applications and integrating LangGraph and LangChain into real-world workflows.

The role emphasizes collaboration with security and engineering teams, mentoring peers, addressing latency and reliability, and delivering robust, maintainable AI components in a hybrid setting.

Qualifications

• 5–7+ years of software engineering experience designing and implementing large-scale production systems.
• Strong Python development experience.
• Hands-on experience with Agentic AI.
• Knowledge of RAG (Retrieval-Augmented Generation).
• Experience with LangGraph and LangChain.

Responsibilities

• Design, develop, test, and document scalable AI solutions and reusable components.
• Build and integrate Agentic AI and RAG-based applications.
• Develop prototypes using emerging AI technologies and integrate successful solutions into production architecture.
• Perform root cause analysis and resolve complex technical issues.
• Identify and address technical debt, scalability, latency, reliability, and security concerns.
• Collaborate with engineering and security teams to deliver high-quality production solutions.
• Mentor junior engineers and promote best engineering practices.

Skills

Python development

Agentic AI

RAG

LangGraph

LangChain

Production systems

Education

Bachelor’s degree in Computer Science/Engineering/related field

Job description

Motion Recruitment in Fort Worth, TX seeks an experienced AI Engineer to design, develop, and deploy scalable AI solutions in production. You will leverage Python and Agentic AI, building RAG-based applications and integrating LangGraph and LangChain into real-world workflows.

The role emphasizes collaboration with security and engineering teams, mentoring peers, addressing latency and reliability, and delivering robust, maintainable AI components in a hybrid setting.