Atharv D.

Key Skills

Software

Data Annotation Tech

Mercor

AWS SageMaker

Top Subject Matter

LLM training data for prompt engineering and RLHF (text/code).

Specialized machine learning dataset curation and semantic labeling (text).

LLM fine-tuning dataset labeling for communication coaching (text).

Top Data Types

Computer Code Programming

Text

Audio

Top Task Types

Fine-tuning

RLHF

Red Teaming

Data Collection

Freelancer Overview

Freelance AI Trainer & Data Annotator — DataAnnotation.tech (50+ hours completed). Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Data Annotation Tech, Mercor, and Other. Education includes Bachelor of Technology, Samrat Ashok Technological Institute (2026) and Higher Secondary Education (Grade 12), CBSE (English) (2022). AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and Entity.

Labeling Experience

Freelance AI Trainer & Data Annotator — Mercor (30+ hours completed)

MercorText

Performed data curation and semantic labeling to optimize datasets for specialized machine learning models. Verified model responses and refined dataset quality through structured labeling and review. Focused on improving relevance and correctness of labeled examples used for model training. • Curated datasets and verified model responses • Applied semantic labeling to improve training signals • Refined dataset entries for specialized model use • Supported dataset optimization for better downstream performance

2024 - Present

Freelance AI Trainer & Data Annotator — DataAnnotation.tech (50+ hours completed)

Data Annotation TechText

Provided prompt-engineering and RLHF-focused evaluation and annotation of complex text and code datasets for training advanced LLMs. Maintained high accuracy by following strict quality guidelines and refining dataset annotations. Performed iterative review of model outputs to improve dataset consistency and alignment. • Annotated and refined text/code examples for LLM training • Evaluated responses using RLHF-style quality criteria • Conducted model response verification and dataset curation • Flagged edge cases, bias, and factual inaccuracies for correction

2024 - Present

AI-Based Communications Coach — College Major Project

OtherTextFine-tuning

Collected, cleaned, and labeled text datasets used to fine-tune a large language model for AI-based communication coaching. Built ground-truth validation labeling workflows to ensure contextual relevance in training data. Developed backend support to log conversation contexts, user metadata, and validation labels for training and QA. • Labeled and curated coaching-oriented text datasets • Prepared data for LLM fine-tuning and relevance • Logged conversation context and user metadata • Implemented ground-truth validation label tracking

2025 - 2026

Education

Work History