Tejas Dhokchaule

Key Skills

Software

CrowdSource

CVAT

Data Annotation Tech

iMerit

Labelbox

OneForma

Scale AI

Surge AI

Telus

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Computer Code Programming

Document

Geospatial Tiled Imagery

Image

Text

Video

Top Task Types

Action Recognition

Audio Recording

Bounding Box

Classification

Computer Programming Coding

Data Collection

Emotion Recognition

Evaluation Rating

Point Key Point

Prompt Response Writing SFT

Red Teaming

RLHF

Segmentation

Text Generation

Text Summarization

Transcription

Translation Localization

Freelancer Overview

I have over two years of hands-on experience in AI training data operations, specializing in data labeling, annotation, and evaluation for large language models (LLMs). My work has focused on RLHF workflows, response ranking, hallucination detection, bias and safety review, and prompt engineering, where I have consistently delivered high-quality datasets and maintained top QA standards. I have contributed to large-scale projects, including robotics and multimodal data curation, and have performed thousands of annotation and ranking tasks for production AI systems. My technical skills include Python, SQL, and analytical reasoning, and I am adept at factual verification, sentiment analysis, and content moderation. I am passionate about improving AI model accuracy and alignment through meticulous data operations and continuous feedback.

Labeling Experience

data labeling

Data Annotation TechTextClassificationText Generation

Worked on large-scale LLM data annotation and response evaluation project for Meta AI models. Performed text classification, pairwise response ranking, hallucination detection, bias evaluation, and factual verification. Annotated and reviewed 3,000+ complex prompts and AI-generated outputs under strict QA benchmarks. Contributed to supervised fine-tuning (SFT) datasets and reinforcement learning from human feedback (RLHF) pipelines. Maintained 95%+ quality accuracy across audit cycles.

Worked on large-scale LLM data annotation and response evaluation project for Meta AI models. Performed text classification, pairwise response ranking, hallucination detection, bias evaluation, and factual verification. Annotated and reviewed 3,000+ complex prompts and AI-generated outputs under strict QA benchmarks. Contributed to supervised fine-tuning (SFT) datasets and reinforcement learning from human feedback (RLHF) pipelines. Maintained 95%+ quality accuracy across audit cycles.

2023 - 2025

Education

Work History