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OpenTrain AI

LangChain v2 Developers Needed for AI Code Review & Evaluation

OpenTrain AI · Remote · Worldwide · Posted Jun 9, 2026

Apply for this job Hourly · $20/hr

About OpenTrain

OpenTrain is a central job board for AI-training and data-labeling work. We aggregate projects from multiple AI companies and labeling platforms so contributors can find relevant roles in one place. Creating an OpenTrain account is free and applying takes only a few minutes.

About AI Training Work

AI training (data labeling / annotation / human feedback) is the human work behind how models learn to be accurate and safe. Tasks include reviewing code and model outputs, annotating examples, and giving structured feedback so models improve over time.

This role sits at the intersection of engineering and evaluation: you will read and judge AI-generated LangChain v2 code and explanations, then provide clear, actionable feedback that helps developers and model trainers alike.

The Role

We are hiring an experienced LangChain v2 developer to perform code review, evaluation, and AI-driven interviews. You will analyze AI-generated prompts, code snippets, and explanations about LangChain v2, label and categorize outputs, identify issues, and suggest improvements aligned with LangChain best practices.

This is a remote, part-time contract role intended for hands-on engineers who can both evaluate technical correctness and communicate recommendations in structured written form.

  • Title: LangChain v2 Code Review & Evaluation Contractor
  • Workload: Less than 20 hours per week (flexible)
  • Employment: Contractor, Part-time
  • Compensation: $20 USD per hour

What You'll Do

Your day-to-day combines technical evaluation with interviewer tasks. You will present candidates with tasks, debug code snippets, and produce structured labels and written feedback on AI-generated LangChain v2 outputs.

  • Conduct AI-driven technical interviews to assess LangChain v2 expertise and communication clarity.
  • Analyze AI-generated LangChain v2 code and explanations, labeling correctness, efficiency, and adherence to best practices.
  • Identify errors, inefficiencies, missing optimizations, and recommend concrete fixes or alternative approaches.
  • Assess prompt chaining, memory usage, retrievers, vector DB integration, and LLM provider configuration.
  • Document feedback clearly and concisely so it can be used as training data or hiring signals.

Interview Guidelines & Example Tasks

You will follow a structured interview checklist to probe candidates' depth of hands-on experience, debugging ability, evaluation skills, and communication. Below are the key assessment areas and a representative example from the project brief.

  • 1. Experience Assessment: Verify real-world LangChain v2 projects (agents, prompt chaining, RAG, vector DBs, LLM integrations).
  • 2. Technical Knowledge Check: Present buggy LangChain v2 code, ask candidate to identify issues and suggest optimizations.
  • 3. AI Evaluation & Labeling: Ask candidates to label AI-generated explanations for accuracy and propose corrections.
  • 4. Communication & Clarity: Request simple explanations of agent workflows and real-world applications.
  • 5. Final Confirmation: Confirm prior experience evaluating AI-generated code, availability, and attention to detail.
  • Example code snippet you may use in interviews: from langchain.llms import OpenAI\n\nllm = OpenAI(model_name="gpt-4")\nresponse = llm("What is the capital of France?")\nprint(response)
  • Example explanation to evaluate: "LangChain v2 requires OpenAI's API to store conversation history in local memory." Ask candidates to correct and justify.

Requirements

You must be able to demonstrate hands-on experience and provide clear, specific examples from your work. This role emphasizes practical debugging and evaluation skills over theoretical knowledge.

  • 5+ years of hands-on experience building LLM-powered applications and structured workflows with LangChain v2.
  • Deep expertise in prompt chaining, memory modules, retrievers, and vector database integrations.
  • Experience integrating LangChain with OpenAI, Hugging Face, or other LLM providers.
  • Proven ability to debug and optimize LangChain implementations, including callbacks, RAG pipelines, and agent workflows.
  • Strong English writing skills; must deliver structured, detailed feedback suitable for labeling and training data.
  • Availability for up to 20 hours per week and comfortable working as a contract, part-time contributor.
  • Willingness to use the project's specified labeling software (listed as 'Other').
  • Worldwide applicants welcome; applicants should be prepared to demonstrate past LangChain v2 projects and troubleshooting examples.

Compensation & Logistics

This role pays $20 USD per hour on a contractor basis. The expected commitment is under 20 hours per week and schedule is flexible. The project is remote and open to worldwide applicants.

The work type is labeled as COMPUTER_CODE_PROGRAMMING and tasks will involve COMPUTER_PROGRAMMING_CODING label types.

  • Pay type: Pay per hour — $20 USD/hour
  • Hours: Less than 20 hours/week (flexible)
  • Employment type: Contractor, Part-time
  • Data type: Computer code / programming; Label types: COMPUTER_PROGRAMMING_CODING

How It Works

Apply through OpenTrain to join this contract role. OpenTrain aggregates AI-training jobs so you can discover and apply quickly; creating an account is free and applications usually take only a few minutes.

If selected, you will be onboarded with the project's evaluation guidelines and given access to the labeling interface and interview materials.