Ram Krishan Saini

Key Skills

Software

CVAT

Top Subject Matter

No subject matter listed

Top Data Types

Text

Top Label Types

Question Answering

Text Generation

Prompt Response Writing SFT

Freelancer Overview

I am a detail-oriented professional with a strong background in applied physics, data analysis, and content development, seeking opportunities in data labeling and AI training data roles. My experience includes developing and editing high-quality academic content for competitive exams like JEE and NEET, where accuracy and clarity were paramount. I am skilled in Python, MATLAB, C, and LaTeX, and have hands-on experience with research projects involving advanced data analysis and annotation, such as my thesis on graphene quantum dots and coursework in lithium-ion battery R&D. My work as an editor and subject matter expert has refined my attention to detail, consistency, and ability to follow complex guidelines, while my leadership roles have strengthened my communication and project management skills. I am passionate about leveraging my technical expertise and analytical mindset to contribute to high-quality AI training datasets across diverse domains.

Labeling Experience

High-Accuracy Image & Text Data Labeling for AI Model Training

CVATTextQuestion AnsweringText Generation

Worked on a comprehensive data labeling and annotation project supporting the training, fine-tuning, and evaluation of machine learning and AI models. The project involved annotating large-scale datasets across both computer vision and natural language processing (NLP) domains. For image datasets, performed bounding box, polygon, and segmentation annotations for objects such as people, vehicles, medical instruments, and retail products, ensuring high precision and pixel-level accuracy where required. For text-based datasets, carried out entity recognition (NER), text classification, and question–answer annotation, including validating and refining human-written Q&A pairs for LLM training and evaluation. Additionally, contributed to prompt creation and prompt–response writing, designing diverse, high-quality prompts to support supervised fine-tuning (SFT) and model behavior alignment. This included crafting clear instructions, realistic user queries, and accurate model responses while

Worked on a comprehensive data labeling and annotation project supporting the training, fine-tuning, and evaluation of machine learning and AI models. The project involved annotating large-scale datasets across both computer vision and natural language processing (NLP) domains. For image datasets, performed bounding box, polygon, and segmentation annotations for objects such as people, vehicles, medical instruments, and retail products, ensuring high precision and pixel-level accuracy where required. For text-based datasets, carried out entity recognition (NER), text classification, and question–answer annotation, including validating and refining human-written Q&A pairs for LLM training and evaluation. Additionally, contributed to prompt creation and prompt–response writing, designing diverse, high-quality prompts to support supervised fine-tuning (SFT) and model behavior alignment. This included crafting clear instructions, realistic user queries, and accurate model responses while

2024 - 2024

Education

Work History