LlamaIndex Developer for AI Code Evaluation

About OpenTrain

OpenTrain AI helps people find and build careers in AI training and data labeling. In this role, you will contribute directly to improving the systems that help AI understand LlamaIndex development, retrieval-augmented generation, and code quality.

• Remote contractor opportunity for candidates in India
• Part-time schedule of less than 20 hours per week
• Pay of $25 USD per hour
• Apply and manage your opportunity through OpenTrain

About AI Code Evaluation

AI training is the human side of building modern artificial intelligence. Technical contributors review model-generated code and explanations, identify mistakes, and provide structured feedback that helps AI produce more accurate and useful results.

• Analyze AI-generated prompts, responses, and code snippets
• Assess accuracy, relevance, and alignment with development best practices
• Label, categorize, and evaluate model outputs
• Help improve how AI handles indexed data and information retrieval

The Role

OpenTrain AI is seeking a LlamaIndex developer to review AI-generated content related to retrieval-augmented generation systems, document indexing, structured data retrieval, and large language model integrations. You will assess whether responses correctly retrieve, structure, and process information from indexed sources.

The listing is categorized as entry level, but the project requires at least 5 years of hands-on LlamaIndex experience. Strong English writing skills are essential because you will provide detailed, structured feedback on technical responses.

• Contractor and part-time engagement
• Less than 20 hours per week
• $25 USD per hour
• English-language work
• Candidates must be located in India

What You'll Do

You will evaluate AI-generated LlamaIndex prompts, responses, explanations, and code snippets. Your feedback should identify errors, inconsistencies, inefficiencies, and missing best practices while explaining how the output can be improved.

• Review LlamaIndex code and technical explanations for accuracy
• Evaluate RAG workflows, document indexing, and structured retrieval
• Assess query optimization, vector database usage, embeddings, and LLM integration
• Check data indexing, document chunking, retrieval optimization, and query routing
• Identify and correct issues in AI-generated code responses
• Label and categorize model outputs consistently
• Explain technical findings clearly in structured English feedback

Requirements

You should have substantial practical experience developing with LlamaIndex and working with RAG systems. Candidates must be able to demonstrate real-world technical depth rather than relying on vague or purely theoretical answers.

• At least 5 years of hands-on LlamaIndex experience
• Experience with retrieval-augmented generation systems
• Knowledge of document indexing and structured data retrieval for LLMs
• Familiarity with vector databases such as FAISS, Pinecone, or ChromaDB
• Experience with embedding models and LLM query performance optimization
• Understanding of vector stores, chunking strategies, and query routing
• Strong written English communication skills
• Ability to identify and fix issues in LlamaIndex code

Technical Interview and Evaluation

The selection process includes an AI-driven technical interview focused on LlamaIndex development and AI evaluation skills. You may be asked to discuss real-world projects, diagnose a deliberately flawed code snippet, and explain how you would design efficient indexing and retrieval workflows.

The interview also evaluates your ability to critique AI-generated responses, communicate complex concepts in accessible English, and explain subjects such as hierarchical retrieval or embedding-based search. Candidates should be prepared to confirm prior experience reviewing AI-generated code or performing code quality evaluation work, along with their availability for structured labeling tasks.

• Discuss hands-on LlamaIndex projects involving RAG and document indexing
• Describe work with vector databases, embeddings, and query optimization
• Identify and fix an issue in a LlamaIndex code example
• Explain efficient data structuring, indexing, and retrieval approaches
• Critique AI-generated code for errors, inefficiencies, and missing practices
• Explain complex LlamaIndex features in clear, accessible English
• Confirm availability and willingness to complete structured labeling tasks

Why Join AI Training Work

AI training offers a flexible way for technical professionals to work on cutting-edge systems from anywhere. Your LlamaIndex expertise can help shape how AI models generate code, explain retrieval workflows, and support developers using modern language-model applications.

• Work remotely with a flexible part-time schedule
• Apply specialized software and AI development knowledge
• Contribute to the quality of emerging AI tools
• Build experience in the growing field of AI training and data labeling

How to Apply

Create a free OpenTrain account, review the opportunity, and apply in minutes. Be ready to demonstrate your practical LlamaIndex experience, technical judgment, and ability to provide precise feedback in English.

• Apply through OpenTrain AI
• Highlight hands-on LlamaIndex and RAG experience
• Prepare examples of indexing, retrieval, and code evaluation work
• Complete the technical interview if selected