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Rusu Biswas

Rusu Biswas

AI Fellow/Trainer - Technology & Internet

USA flag
New York City, Usa
$20.00/hrEntry LevelInternal Proprietary Tooling

Key Skills

Software

Internal/Proprietary Tooling

Top Subject Matter

No subject matter listed

Top Data Types

AudioAudio
DocumentDocument
ImageImage
TextText
VideoVideo

Top Label Types

Audio Recording
Bounding Box
Classification
Entity Ner Classification
Evaluation Rating
Object Detection
Prompt Response Writing SFT
Question Answering
Text Generation
Text Summarization
Transcription

Freelancer Overview

I am passionate about technology, learning, and contributing to the advancement of AI through high-quality data labeling and annotation. My experience as an AI Fellow/Trainer involved developing and evaluating prompts for large language models (LLMs), analyzing outputs for accuracy and clarity, and providing detailed feedback to improve model understanding in specialized domains. I have a strong foundation in critical thinking, communication, and teamwork, with additional experience in research and prompt engineering for NLP tasks. My adaptability and attention to detail, honed through both technical and customer-facing roles, enable me to efficiently handle complex data annotation projects while ensuring quality and compliance. I am proficient in using productivity tools like Microsoft Excel and am fluent in both English and Bengali, allowing me to work effectively in diverse teams and multilingual environments.

Entry LevelEnglishBengali

Labeling Experience

Audio Transcription and Speech Data Annotation for AI Training

Internal Proprietary ToolingAudioTranscription
Performed audio transcription and speech data labeling tasks to support speech recognition and conversational AI training. Reviewed audio recordings to produce accurate, guideline-compliant transcripts while identifying contextual meaning, speaker intent, and formatting requirements. Conducted quality verification checks to ensure transcription accuracy and dataset usability for model training applications.

Performed audio transcription and speech data labeling tasks to support speech recognition and conversational AI training. Reviewed audio recordings to produce accurate, guideline-compliant transcripts while identifying contextual meaning, speaker intent, and formatting requirements. Conducted quality verification checks to ensure transcription accuracy and dataset usability for model training applications.

2025

Video Content Annotation and AI Output Evaluation

Internal Proprietary ToolingVideoEvaluation Rating
Reviewed and labeled video-based data used for AI training and validation by analyzing visual sequences, contextual accuracy, and classification criteria. Evaluated AI-generated or tagged outputs for alignment with annotation guidelines and quality standards. Maintained consistency across time-based content labeling tasks while ensuring accurate contextual interpretation and structured dataset contribution.

Reviewed and labeled video-based data used for AI training and validation by analyzing visual sequences, contextual accuracy, and classification criteria. Evaluated AI-generated or tagged outputs for alignment with annotation guidelines and quality standards. Maintained consistency across time-based content labeling tasks while ensuring accurate contextual interpretation and structured dataset contribution.

2025

Image Annotation and Visual Data Classification for AI Model Training

Internal Proprietary ToolingImageEntity Ner Classification
Performed image labeling and classification tasks to support computer vision model training and validation. Categorized visual content according to predefined taxonomy guidelines and conducted object identification and annotation tasks where applicable. Maintained high accuracy standards through guideline adherence and quality control review processes. Supported dataset consistency and training reliability through careful visual verification and structured annotation workflows.

Performed image labeling and classification tasks to support computer vision model training and validation. Categorized visual content according to predefined taxonomy guidelines and conducted object identification and annotation tasks where applicable. Maintained high accuracy standards through guideline adherence and quality control review processes. Supported dataset consistency and training reliability through careful visual verification and structured annotation workflows.

2025

Supervised Fine-Tuning (SFT) Prompt and Response Dataset Creation

Internal Proprietary ToolingTextPrompt Response Writing SFT
Designed prompts and authored high-quality reference responses used for supervised fine-tuning of large language models. Generated diverse, instruction-focused training examples to improve model reasoning, factual accuracy, and user intent alignment. Followed detailed annotation guidelines to ensure consistency, clarity, and structural quality across training datasets. Participated in quality review cycles to maintain dataset reliability and training effectiveness.

Designed prompts and authored high-quality reference responses used for supervised fine-tuning of large language models. Generated diverse, instruction-focused training examples to improve model reasoning, factual accuracy, and user intent alignment. Followed detailed annotation guidelines to ensure consistency, clarity, and structural quality across training datasets. Participated in quality review cycles to maintain dataset reliability and training effectiveness.

2025

Multi-Domain LLM Response Evaluation and Quality Rating

Internal Proprietary ToolingTextEvaluation Rating
Evaluated large language model (LLM) outputs across multiple subject areas by rating response accuracy, reasoning quality, instruction compliance, and clarity. Reviewed generated content against structured annotation guidelines and scoring frameworks to assess factual correctness, logical consistency, and completeness. Conducted independent research to verify claims and identify hallucinations or misleading outputs. Maintained consistency across high-volume evaluation workflows and contributed structured feedback to support model training and dataset refinement.

Evaluated large language model (LLM) outputs across multiple subject areas by rating response accuracy, reasoning quality, instruction compliance, and clarity. Reviewed generated content against structured annotation guidelines and scoring frameworks to assess factual correctness, logical consistency, and completeness. Conducted independent research to verify claims and identify hallucinations or misleading outputs. Maintained consistency across high-volume evaluation workflows and contributed structured feedback to support model training and dataset refinement.

2025

Education

A

Aviation High School

Advanced Regents High School Degree, Aviation Mechanical and General Education

Advanced Regents High School Degree
2021 - 2025

Work History

P

Panera Bread

Crew Member

New York City
2025 - Present
D

DCYD

Intern

Richmond Hill
2023 - 2023