Laxman S.

Key Skills

Software

Other

CVAT

Label Studio

Prodigy

SuperAnnotate

Labelbox

Top Subject Matter

AI model evaluation

RLHF data preparation

quality assurance for annotated datasets

Top Data Types

Text

Image

Document

Top Task Types

RLHF

Entity (NER) Classification

Prompt + Response Writing (SFT)

Bounding Box

Classification

Text Generation

Question Answering

Text Summarization

Object Detection

Computer Programming/Coding

Evaluation/Rating

Freelancer Overview

Core Data Labeling Skills (Code/AI training oriented portfolio project). Brings 1+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal, Proprietary Tooling, and Other. Education includes Bachelor of Technology, Indian Institute of Technology (BHU), Varanasi (2025) and Higher Secondary Education Certificate (XII), Balaji Public School, Sanpa (2021). AI-training focus includes data types such as Text and labeling workflows including RLHF and Entity (NER) Classification.

Labeling Experience

AI-Enhanced Resume and Job Matcher (data labeling/annotation for NLP models)

OtherTextEntity (NER) Classification

Curated, cleaned, and accurately labeled resume text datasets to train NLP and machine learning models for high-accuracy job matching and automated candidate profile classification. Implemented a workflow that covered data extraction, text labeling, skill categorization, and candidate-job matching while aligning labels to downstream model tasks. Performed structured data preparation to support entity recognition and classification objectives for résumé analytics. • Labeled resume text for skill extraction, entity recognition, and profile classification. • Categorized extracted skills and mapped candidates to job requirements. • Prepared cleaned datasets suitable for NLP/ML model training. • Supported job-matching features using labeled textual signals.

2024

Core Data Labeling Skills (Code/AI training oriented portfolio project)

TextRLHF

Worked on AI model evaluation and RLHF-related preparation by applying algorithmic precision and detail-oriented quality assurance practices to annotation workflows. Focused on maintaining accuracy, consistency, and guideline adherence during complex AI-data tasks intended for reinforcement learning feedback scenarios. Used structured documentation and validation steps to reduce edge-case errors and improve overall dataset reliability. • Curated and validated data inputs for RLHF evaluation objectives. • Ensured consistency checks and edge-case identification during labeling. • Emphasized high-precision quality assurance aligned with strict guidelines. • Applied timemanagement and structured documentation to sustain annotation quality.

2024

Education

Work History