Python Infrastructure Engineer for LLM Training

The work

You will design and maintain the infrastructure used in LLM training and agent evaluation. The work includes reusable repositories, scoring pipelines, secure sandboxes, task environments, and developer environments.

You will also support researchers who use these tools and help keep the code reliable, secure, and easy to maintain.

• Architect secure sandboxes and task environments.
• Build and maintain CI/CD pipelines.
• Write clean, test-driven Python code with pytest.
• Containerize services with Docker.
• Work with FastAPI and Flask back ends.
• Use GitHub Actions or similar continuous integration tools.
• Support researchers and mentor teammates.
• Use AI coding tools such as Cursor or Claude Code when appropriate.

What it pays and takes

This is a remote, part-time contractor role. The description lists experience-based hourly tiers, while the structured listing shows an hourly rate of $12.50 USD.

• Pay tiers in the role description: Junior $9/hour, Middle $12/hour, and Senior $16/hour.
• Structured listing rate: $12.50 USD per hour.
• Time commitment: 20+ hours per week.
• Work arrangement: Remote contractor, part time.
• Location: Candidates must be in the Asia-Low region, with supported countries including Bangladesh, Georgia, India, Indonesia, Malaysia, Pakistan, the Philippines, Sri Lanka, Thailand, and Vietnam.
• Language: English.
• Experience: 5+ years of professional Python experience.
• Required skills: Strong Linux command-line skills, Python engineering, and familiarity with FastAPI or Flask.
• Required tooling knowledge: GitHub Actions or a similar CI system.
• Helpful strengths: Mentoring teammates and using AI coding tools.
• Selection requirement: Complete a HackerRank assessment and a platform coding test.

How it works

You apply on OpenTrain, and the employer reviews applications there.

About AI training work

AI training work is the human side of building artificial intelligence. Engineers and other specialists create the tools, datasets, tests, and evaluations that help AI systems learn and perform reliably, which is why people with strong technical experience are needed.