Python Infrastructure Engineer for LLM Training

You will design and maintain the infrastructure used to train and evaluate large language models. The work includes building secure sandboxes, task environments, reusable code repositories, scoring pipelines, and developer environments for agent performance evaluation.

You will also support researchers who use these tools and help keep the development process reliable. Clean, test-driven Python code and practical automation are central to the role.

• Architect and maintain secure sandboxes and task environments.
• Build and support CI/CD pipelines for Python services and tooling.
• Write clean, test-driven code with pytest.
• Containerize services with Docker.
• Develop back ends with FastAPI or Flask.
• Use GitHub Actions or similar CI tools.
• Support researchers and help maintain developer environments.
• Mentor teammates and use AI coding tools such as Cursor or Claude Code when useful.

What It Pays And Takes

This is a fully remote, part-time contractor role for candidates in the listed countries. The work requires professional Python experience and strong command-line skills in Linux.

• Pay: $12.50 USD per hour.
• Time: At least 20 hours per week.
• Work arrangement: Fully remote contract work.
• Open to candidates in Bangladesh, Georgia, India, Indonesia, Malaysia, Pakistan, the Philippines, Sri Lanka, Thailand, and Vietnam.
• Language: English proficiency is required.
• Experience: Intermediate level, with at least five years of professional Python experience.
• Skills: Python, Linux command line, pytest, Docker, FastAPI or Flask, and GitHub Actions or similar CI tools.
• Helpful but optional: Experience mentoring teammates and using AI coding tools.
• Selection includes a HackerRank assessment and a platform coding test before recruiter interviews.

How It Works

Apply on OpenTrain with your resume. The employer reviews applications there.

About AI Training Work

AI training is the human work behind systems that learn from examples, including writing code, checking model results, and evaluating how AI agents perform. People with software engineering and machine learning skills help make these systems more reliable and useful.