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LLM Evaluator - Portuguese Language (Brazilian dialect) - Remote

Posted Yesterday
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Freelance
USA

Overview

Chemin, TDCX’s AI-focused subsidiary, is launching a new gigwork opportunity for individuals eager to earn while contributing to cutting-edge AI projects.

In Short

  • Fully remote, task-based role
  • Flexibility to increase income at your own pace
  • Work on advanced technology for financial solutions
  • Review conversations as an LLM Evaluator
  • Perform sentiment analysis and emotion taxonomy
  • Deliver high-quality annotations with 90% accuracy
  • Communicate effectively with Project Managers
  • Maintain efficiency and reliability in a remote work environment
  • Meet key deadlines while maintaining quality
  • Onboarding and training provided

Requirements

  • Native Portuguese speaker from Brazil
  • Passion for technology, finance, or linguistics
  • Ability to tag emotions and sentiments consistently
  • Strong communication skills
  • Attention to detail
  • Ability to work independently in a remote environment
  • Commitment to quality and consistency
  • Willingness to learn and adapt
  • Experience with AI or fintech is a plus
  • Reliable internet connection

Benefits

  • Flexible working hours
  • Opportunity to work on innovative AI projects
  • Competitive pay based on task completion
  • Gain experience in the AI and fintech sectors
  • Work from anywhere
Chemin logo

Chemin

Chemin is a subsidiary of TDCX that focuses on artificial intelligence, offering innovative gig work opportunities for individuals interested in contributing to advanced AI projects. Collaborating with leading global companies, Chemin is currently partnering with a prominent fintech client to enhance next-generation AI solutions. The company provides a fully remote, task-based role for Large Language Model (LLM) Evaluators, allowing individuals, particularly those from Brazil, to earn income flexibly while shaping the future of financial technology. Chemin emphasizes high-quality contributions, offering training and support to ensure success in improving the accuracy of AI systems.

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