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LangChain v2 Developer for AI Code Review

Review AI-generated LangChain v2 code, prompts, and explanations for correctness and best practices. This part-time contractor role pays $20 per hour and is open to candidates in India with strong English skills.

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OpenTrain AI

Coding & Software

Remote Hourly · $20/hr

$20/hr

Compensation

1 country

Eligibility

Entry

Experience

Mar 10, 2025

Posted

Open to applicants in

India

The work

You will review AI-generated prompts, responses, code snippets, and explanations related to LangChain v2. Your feedback will help assess whether the material is correct, efficient, usable, and aligned with good LangChain v2 workflows.

  • Evaluate AI-generated code and explanations for correctness and efficiency.
  • Label, categorize, and assess code snippets and developer guidance.
  • Check prompts and responses against LangChain v2 best practices.
  • Find errors, inefficient approaches, and missing optimizations.
  • Write structured, detailed feedback and suggest improvements.

What it pays and takes

This is a part-time contract role. The listing is marked entry level, but the role requires substantial hands-on experience with LangChain v2 and LLM-powered applications.

  • Pay: $20 per hour.
  • Time: Less than 20 hours per week.
  • Work arrangement: Part-time contractor.
  • Location: India only.
  • Language: English.
  • Experience: 5+ years of hands-on experience with LLM-powered applications, AI agents, and LangChain v2.
  • Technical knowledge: Prompt chaining, memory modules, retrievers, vector databases, and structured workflows.
  • Integrations: Experience connecting LangChain with OpenAI, Hugging Face, or other LLM providers.
  • Communication: Strong English writing skills for detailed, structured feedback.

How it works

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

About AI training work

AI training work uses human-written examples, reviews, and ratings to improve how AI systems respond. Experienced contributors are paid to catch mistakes, explain what good output looks like, and help make models more useful.

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