Adithya K.

Key Skills

Software

Other

Top Subject Matter

Search relevance evaluation and webpage quality assessment for Bing (UHRS).

Multilingual NLP data annotation for sentiment

Intent Domain Expertise

Top Data Types

Text

Audio

Image

Document

Top Task Types

Entity (NER) Classification

Transcription

Freelancer Overview

AI Data Annotator & Search Quality Rater — Microsoft UHRS via vendor platform (Remote, India). Core strengths include Telus, Internal, and Proprietary Tooling. Education includes Bachelor of Business Administration, Indira Gandhi National Open University (IGNOU) (2025). AI-training focus includes data types such as Text and Audio and labeling workflows including Evaluation, Rating, and Entity (NER) Classification.

Labeling Experience

Voice & Audio Annotation Practice (Self-directed/ongoing)

OtherAudioTranscription

Produced voice and audio annotations for English and Malayalam by transcribing audio samples and applying speaker diarization and timestamp tagging. Labeled voice recordings for accent clarity, speech fluency, and emotional tone to support downstream virtual assistant training needs. Used native bilingual proficiency to produce accurate multilingual transcriptions aligned to annotation practices.• Transcribed English and Malayalam audio with timestamps. • Applied speaker diarization to separate speakers. • Labeled accent, fluency, and emotional tone from recordings. • Produced multilingual, rubric-aligned audio annotations.

2024 - Present

Multilingual Text Data Annotation — Microsoft UHRS (AI Data Annotator)

TextEntity (NER) Classification

Annotated multilingual text datasets in English and Malayalam for sentiment analysis, intent classification, and entity recognition tasks. Followed task-specific annotation guidelines carefully to ensure consistent label quality and rubric compliance. Maintained reliability while handling confidential content under NDA protocols within the evaluation workflow.• Labeled text for sentiment, intent, and NER across English and Malayalam. • Ensured guideline-based, rubric-compliant annotations. • Reconciled ambiguity by applying defined rules and standards. • Supported dataset quality through consistent inter-rater performance.

2023 - Present

AI Data Annotator & Search Quality Rater — Microsoft UHRS via vendor platform (Remote, India)

TelusText

Evaluated Microsoft Bing search query-result pairs using rubric-based criteria for relevance, accuracy, and user satisfaction in a remote UHRS setting. Conducted webpage quality assessments across large volumes, judging trustworthiness, content depth, E-E-A-T signals, and spam indicators. Performed side-by-side webpage comparison to decide which page best satisfies a target user query, supporting search ranking improvements.• Rated relevance of query-result pairs against quality rubrics. • Assessed webpage quality including E-E-A-T and spam signals. • Selected better-matching page in side-by-side comparisons. • Applied guidelines to maintain high inter-rater reliability.

2023 - Present

Education

Work History