Prothenic X.

Key Skills

Software

Label Studio

Other

Top Subject Matter

Geographic/localized data quality assurance for AI recommendations

Entity-level image annotation for multimodal AI (Instagram)

Top Data Types

Image

Text

Video

Top Task Types

Bounding Box

Classification

Text Generation

Text Summarization

Evaluation/Rating

Data Collection

Prompt + Response Writing (SFT)

Entity (NER) Classification

Freelancer Overview

Ara Mu Personalization Map Rater [AI Project] — AI Data Operations / Ground Truth QA. Brings 7+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other and Label Studio. Education includes Bachelor of Engineering, Rajiv Gandhi Proudyogiki Vishwavidyalay (2017) and Higher Secondary, CG State Board of Secondary Education (2013). AI-training focus includes data types such as Geospatial, Tiled Imagery, and Image and labeling workflows including Evaluation, Rating, and Entity (NER) Classification.

Labeling Experience

Personalization Map Rater (Ara Mu) — Welo Data

Other

Evaluated and audited geographic/localized data inputs for the Ara Mu Map Personalization Rater project. Produced ground-truth annotations by analyzing complex string scenarios and context logic constraints. Ensured search relevance and algorithmic accuracy while meeting service time and quality benchmarks. • Rated and validated map-related data quality • Performed ground-truth annotation analysis for string/context logic • Checked outputs against relevance and accuracy expectations • Maintained performance against service-time and quality benchmarks

2026 - Present

IG Entity Image Tagging – Data Annotation Specialist [AI Project] (Outlier AI via Multimango)

Label StudioImageEntity (NER) Classification

Completed IG entity image tagging tasks using a crowdsourced annotation workflow distributed through the Multimango tasking platform for Outlier AI. Used Label Studio to annotate and classify entities within Instagram images with structured entity-level tags aligned to project guidelines and ontology definitions. Reviewed and validated peer annotations to improve quality and inter-annotator agreement, while applying RLHF principles by evaluating and ranking AI-generated outputs. • Tagged entities such as objects, people, brands, and contextual elements • Applied consistent annotation schema and ontology-aligned labels • Performed peer QA to reduce labeling errors • Evaluated/ranked AI outputs to support RLHF-driven behavior alignment

2024 - 2025

Education

Work History