Description
Alongside partners with organizations to drive digital transformation and build high-performing tech teams.
We are looking for a detail-oriented LLM QA Contractor to join an AI validation project focused on improving the quality of Large Language Model (LLM) outputs.
You will review photos and delivery instructions, compare them with AI-generated responses, and ensure outputs are accurate, complete, and aligned with established quality guidelines. Your feedback will directly contribute to developing safer and more reliable AI experiences.

Responsibilities:
- Review AI-generated outputs against source images and delivery instructions.
- Verify factual accuracy, completeness, and adherence to annotation guidelines.
- Identify and classify issues such as hallucinations, missing information, incorrect interpretations, or policy violations.
- Provide concise and structured feedback to support continuous model improvement.
- Follow detailed Standard Operating Procedures (SOPs) and participate in calibration sessions.
- Track your work using annotation tools and spreadsheets.




- Escalate ambiguous cases or guideline gaps when needed.
- Consistently meet quality, accuracy, and productivity targets.

Requirements
- Excellent attention to detail and a structured, analytical mindset.
- Previous experience in Content QA, Data Labeling, Trust & Safety, Content Moderation, or similar quality assurance roles.
- Strong visual analysis and reading comprehension skills.
- Comfortable working with web-based tools, spreadsheets, and annotation platforms.
- Reliable, organized, and able to handle repetitive tasks while maintaining a high level of accuracy.
- Strong written communication skills in English.
- Able to handle confidential and user-generated content responsibly.

Benefits
- Remote: Work from anywhere.
- Contract: Freelance / Service Agreement.
- Initial duration: 90-day contract with the possibility of extension.
- Schedule: Part time (4 hours per day).
- Weekly

📌 LLM QA Contractor (India)
🏢 Alongside
📍 India