> Markdown version of [/jobs/ext/4317787-ai-engineer](https://www.wearedevelopers.com/jobs/ext/4317787-ai-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# AI Engineer

- **Company:** Gamestop, Inc.
- **Location:** Grapevine, TX, United States
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Automated Storage and Retrieval Systems, Microsoft Azure, Cloud Computing, Computer Programming, Databases, Python (Programming Language), OpenAI, Systems Integration, TypeScript, Pinecone, Qdrant, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Prompt Engineering, Llamaindex, Agentic-AI, Fastapi, CrewAI, Weaviate, Claude, GPT, Automation Anywhere, Docker, ChromaDB
- **Published:** October 22, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=6ad3632366e839cb

## About the Role

Must-Haves

 * Proven experience personally building and deploying AI agents in production environments
 * Strong software engineering fundamentals and coding proficiency
 * Deep understanding of LLM applications, prompt engineering, agent architectures, and AI workflows
 * Experience integrating AI systems with APIs, databases, and external services
 * Demonstrated ability to independently deliver projects from idea to execution
 * Strong problem-solving skills and a bias toward action

Preferred Experience

Experience with tools and technologies such as:

 * LangChain
 * LangGraph
 * CrewAI
 * LlamaIndex
 * OpenAI APIs
 * Anthropic Claude
 * Vector databases (Pinecone, Weaviate, Chroma, Qdrant)
 * RAG architectures
 * Multi-agent systems
 * Python
 * TypeScript
 * FastAPI
 * Docker
 * Cloud platforms (AWS, Azure, or GCP)

## Description

* Develop agent architectures using modern LLM frameworks and orchestration tools
 * Build integrations with APIs, databases, internal tools, and external systems
 * Rapidly prototype new AI-driven products and iterate based on real-world feedback
 * Create scalable solutions leveraging large language models, retrieval systems, and automation frameworks
 * Optimize performance, reliability, and cost across AI applications
 * Own projects from concept to deployment with minimal oversight
 * Solve complex technical challenges in fast-moving, high-impact environments