Associate Systems Engineer - AutoZone

We are looking for a passionate and driven Associate Systems Engineer to join our AI development team. In this role, you will assist in the design, development, and deployment of intelligent applications, including large language model (LLM)-powered chatbots and internal tools. You will work closely with senior engineers and technical program managers to build and evaluate features for initiatives like our "Store Assist AI" and logistics tracking systems.

This is an excellent opportunity for an early-career engineer who is eager to learn, build enterprise-grade AI applications, and make a tangible impact in the retail technology space.

AI & Agent Development: Assist in the development, configuration, and integration of AI-powered conversational agents and specialized sub-agents (e.g., store assistance, shipment tracking).

Prompt Engineering & RAG: Help implement and tune Retrieval-Augmented Generation (RAG) pipelines. Assist in evaluating model performance using internal benchmarks (e.g., EnterpriseRAG).

Testing & QA: Support the QA process for AI agents, analyzing chat sessions and application logs to identify areas for improvement, reduce hallucinations, and enhance user experience.

Code Implementation & Tooling: Write clean, maintainable code (primarily in Python). Utilize modern developer tools (VS Code, Git, CLI tools) and AI coding assistants to optimize development workflows.

Collaboration: Participate in sprint planning, code reviews, and technical discussions. Work with cross-functional teams to integrate AI models into existing AutoZone UIs and backend systems.

Continuous Learning: Stay up-to-date with the latest advancements in LLMs, AI frameworks, and best practices for building self-improving agents.

Education: Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field (or equivalent practical experience).

Programming Skills: Proficiency in Python and familiarity with standard data science/ML libraries (e.g., Pandas, NumPy, LangChain, or similar LLM frameworks).

AI/ML Knowledge: Solid fundamental understanding of machine learning concepts, natural language processing (NLP), and how Large Language Models (LLMs) operate.

Development Tools: Experience with version control (Git) and modern IDEs (VS Code). Familiarity with command-line interfaces (CLI).

Analytical Skills: Ability to read and interpret technical documentation, research papers, and application logs to troubleshoot and optimize systems.

Communication: Strong written and verbal communication skills; ability to explain technical concepts clearly to team members.

Nice-to-Haves (Bonus)

Hands-on experience or academic projects involving Retrieval-Augmented Generation (RAG).

Experience interacting with APIs from major AI platform providers (e.g., OpenAI, Anthropic/Claude, Google Gemini).