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# Junior AI Systems Engineer

- **Company:** Buildable AI LLC
- **Location:** Lake Forest, CA, United States
- **Experience:** Starter
- **Salary:** $73,437.0 - $88,440.0
- **Contract:** Permanent contract
- **Skills:** Clean Code Principles, JavaScript (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Databases, Software Debugging, Python (Programming Language), Software Tools, Systems Integration, TypeScript, Large Language Models, Free and Open-Source Software, Automation Anywhere
- **Published:** June 12, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=d1af444620eaf750

## About the Role

Do you have experience in TypeScript?, * Demonstrable AI Experience: A strong portfolio of personal projects, open-source contributions, or past roles where you heavily integrated AI/LLM APIs.

 * Solid Programming Foundation: Strong skills in backend languages (Python, JavaScript/TypeScript) and a deep comfort level with debugging complex, multi-system issues.
 * Fluency in AI Tools: You must already use AI tools daily to accelerate your coding process and troubleshoot errors.
 * Analytical Problem Solving: An obsessive curiosity about how AI systems communicate and a persistent drive to refine those workflows.

## Description

As a Junior AI Systems Engineer at Buildable Ai, you will be the boots on the ground for our core technology refinement. Working directly under our Senior Engineers, you will be in the trenches making sure our AI systems are talking to each other effectively. You will be responsible for implementing integrations, testing system outputs, and using AI tools to write the connective tissue that powers our platform.

Core Responsibilities

 * Systems Integration: Write the reliable, scalable code that links our internal AI models with external tools, APIs, and databases.
 * AI-Assisted Execution: Use the latest AI coding assistants to write clean code rapidly, taking raw architectural concepts and turning them into functional features for buildable.global.
 * Testing & Refinement: Rigorously test AI workflows, identify hallucinations or communication breakdowns between systems, and implement fixes to improve accuracy.
 * Continuous Learning: Stay hyper-current on the bleeding edge of AI tools and models, bringing new efficiency strategies to the engineering team.


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