Harsh Vardhan

Key Skills

Software

Mercor

Top Subject Matter

No subject matter listed

Top Data Types

Text

Top Label Types

Question Answering

Text Generation

Freelancer Overview

I have over six years of experience in customer service and data annotation roles, specializing in delivering high-quality support and AI training data. My work as an AI Annotator and Quality Analyst involved reviewing and validating AI-generated outputs, ensuring accuracy and adherence to structured guidelines. I have a strong eye for detail, analytical problem-solving skills, and experience collaborating with QA teams to maintain consistency in labeling and annotation processes. My background includes extensive use of CRM systems, documentation, and tools such as Salesforce, Google Analytics, and dedicated labeling platforms. I am adept at explaining complex processes in simple terms, maintaining data integrity, and supporting projects in domains like SaaS, gaming, and e-commerce. My focus on quality, process adherence, and continuous improvement ensures I deliver reliable and valuable training data for AI and machine learning initiatives.

Labeling Experience

Data Annotator Quality Analyst

MercorTextQuestion AnsweringText Generation

Acted as a critical, high-level reviewer within the AI development lifecycle, ensuring that data labeled for machine learning—particularly Large Language Models (LLMs)—meets strict accuracy, consistency, and safety standards. Quality Assurance & Validation: Reviewing, auditing, and correcting datasets (text, image, video, code) produced by other annotators to ensure compliance with project specifications. Edge Case Resolution: Analyzing ambiguous, difficult, or anomalous data points and making high-level judgment calls based on complex, multi-page, or evolving, guidelines. Evaluation (SxS): Performing side-by-side (SxS) evaluations of AI-generated content to assess quality, relevance, factual accuracy, and safety.

Acted as a critical, high-level reviewer within the AI development lifecycle, ensuring that data labeled for machine learning—particularly Large Language Models (LLMs)—meets strict accuracy, consistency, and safety standards. Quality Assurance & Validation: Reviewing, auditing, and correcting datasets (text, image, video, code) produced by other annotators to ensure compliance with project specifications. Edge Case Resolution: Analyzing ambiguous, difficult, or anomalous data points and making high-level judgment calls based on complex, multi-page, or evolving, guidelines. Evaluation (SxS): Performing side-by-side (SxS) evaluations of AI-generated content to assess quality, relevance, factual accuracy, and safety.

2025 - 2025

Education

Work History