About OpenTrain

OpenTrain is the #1 platform for finding and building careers in AI training and data labeling. We help people start and grow careers teaching AI by connecting contributors with projects, letting them build a profile, and apply quickly. Creating an OpenTrain account is free.

Our work powers how modern AI systems learn from human examples — and we hire contributors directly into projects that shape model behavior, quality, and safety.

About AI training and why this work matters

AI training (aka data labeling or human feedback work) is the human side of building AI. Contributors annotate outputs, evaluate responses, and correct model-generated code so models improve over time.

These roles are highly flexible and often fully remote. They let you work part-time, influence state-of-the-art systems, and apply practical technical expertise to real-world AI problems.

• 100% remote — work from anywhere with internet.
• Flexible part-time hours — fit around other work or life commitments.
• Direct impact — your feedback improves how AI systems code, reason, and help developers.

The role

We are hiring experienced LangChain v2 developers to evaluate AI-generated LangChain code, explanations, and workflows, and to run structured technical interviews that screen other LangChain candidates.

This is a part-time, contractor role (<20 hours/week). You will label and categorize AI outputs, identify errors or inefficiencies in LangChain v2 implementations, recommend improvements, and assess candidates’ hands-on skills and communication clarity.

• Employment type: Contractor, Part-time.
• Time commitment: Less than 20 hours/week.
• Rate: $20 USD per hour.
• Location: Remote — worldwide.

What you'll do (day-to-day)

Your primary work is technical evaluation and structured interviewing. You will read AI-generated prompts, code snippets, and explanations about LangChain v2 and deliver concise, actionable labels and feedback.

When conducting interviews, you will probe candidates’ real-world LangChain experience, debug code snippets with them, and score responses for correctness, depth, and clarity.

• Analyze AI-generated LangChain v2 prompts, code, and explanations for correctness and best practices.
• Label and categorize outputs (e.g., correct/incorrect, efficient/inefficient, missing optimizations).
• Identify bugs, inefficiencies, and incorrect assumptions in code and explanations.
• Provide structured suggestions and clear reasoning to improve workflows (prompt chaining, memory, retrievers, RAG).
• Conduct technical interviews that include debugging exercises, scenario questions, and communication assessments.

Key skills & technical requirements

All facts about technical expectations come from the project brief: we require deep, hands-on experience with LangChain v2 and related LLM integrations, plus the ability to write clear English feedback.

You must be able to evaluate advanced LangChain concepts, such as prompt chaining, memory modules, retrievers, vector databases, and integrations with OpenAI, Hugging Face, or other LLM providers.

• 5+ years hands-on experience working with LLM-powered applications and LangChain v2.
• Deep knowledge of prompt chaining, memory strategies, retrievers, and vector DB integrations.
• Experience optimizing RAG pipelines and LangChain declarative agent workflows.
• Strong English writing skills — ability to explain technical issues concisely and structure feedback.
• Comfort debugging Python-based LangChain code and explaining fixes clearly during interviews.

Interview & evaluation process you'll run

Interviewers will follow a structured AI-driven interview plan that probes experience, debugging ability, evaluation skills, and communication clarity. You will use provided prompts and code snippets and ask follow-ups to probe depth.

Expect to present candidates with bugged LangChain code, ask them to identify and fix issues, and evaluate their ability to explain agent workflows, memory/retrieval strategies, and integration choices.

• Confirm real-world LangChain v2 project experience and probe for concrete challenges and solutions.
• Give a code snippet with an error; ask candidate to find and explain the problem and the optimization.
• Ask candidates to evaluate AI-generated LangChain explanations and provide corrected statements and improvements.
• Reject candidates who only provide theoretical answers without hands-on detail or who fail to clearly communicate technical reasoning.

How we measure success

Success means consistent, high-quality labeling and interview assessments: clear, reproducible feedback that helps improve AI outputs and identifies skilled LangChain practitioners.

You'll be judged on accuracy of your annotations, clarity of written feedback, and ability to separate superficial answers from demonstrable hands-on experience during interviews.

• Timely, consistent labeling and feedback quality.
• Ability to surface critical issues and propose practical fixes.
• Clear, structured interview notes that justify pass/fail decisions.

Application details & next steps

To apply, create an OpenTrain profile (free) and submit your experience summary focusing on real LangChain v2 projects, tools used (vector DBs, LLM providers), and a short example of a debugging/optimization you performed.

Because this role is evaluation-heavy, your application should demonstrate clear written communication in English and confirm availability for part-time contractor work at $20/hr.

• Pay: $20 USD per hour, PAY_PER_HOUR.
• Schedule: Less than 20 hours per week; remote, worldwide.
• Provide concrete sample(s) of LangChain v2 work or a brief case study in your application.