Open Source AI Engineer for Cloud AI Workflows

Webhosting; Colombia; On-site; COP 70,000,000 - 110,000,000; Full time

Job summary

Backblaze is seeking a Software Engineer to build and maintain open source AI integrations and example applications that demonstrate how Backblaze B2 supports AI workflows. You will work with Python, TypeScript, Go, and Rust, contribute SDK extensions, notebooks, and reference implementations while using AI coding agents to accelerate development.

Collaboration with Product and QA ensures real customer AI usage patterns and robust testing.

Qualifications

• Experience building production-quality open source integrations.
• Strong understanding of AI workflows and data orchestration.
• Ability to contribute to documentation, tests, and reproducible workflows.

Responsibilities

• Implement and maintain open source AI integrations, frameworks and examples using languages commonly used in the open source ecosystem (Python, TypeScript, Go, Rust).
• Build and maintain public sample applications, SDK extensions, notebooks, and reference implementations demonstrating AI and cloud storage best practices.
• Use AI-assisted development tools to accelerate implementation while validating correctness, security, and performance.
• Contribute to open source projects through code contributions, issue triage, reviews, and ongoing maintenance.
• Ensure all open source assets meet high standards for testing, documentation, reproducibility, and developer usability.
• Maintain clean, well-structured GitHub repositories intended for external adoption and community contribution.

Skills

Python

TypeScript

Go

Rust

Open Source

AI Workflows

Tools

Backblaze B2 SDKs

REST APIs

Jupyter

Google Colab

Claude Code

Antigravity

Codex

Job description

Backblaze is seeking a Software Engineer to build and maintain open source AI integrations and example applications that demonstrate how Backblaze B2 supports AI workflows. You will work with Python, TypeScript, Go, and Rust, contribute SDK extensions, notebooks, and reference implementations while using AI coding agents to accelerate development.

Collaboration with Product and QA ensures real customer AI usage patterns and robust testing.