Ritam D.

Key Skills

Software

Other

Top Subject Matter

Natural Language Processing

Documents Domain Expertise

Conversational AI

Top Data Types

Text

Document

Top Task Types

Data Collection

Bounding Box

Object Detection

Text Generation

Text Summarization

RLHF

Fine-tuning

Question Answering

Evaluation/Rating

Classification

Entity (NER) Classification

Transcription

Red Teaming

Prompt + Response Writing (SFT)

Freelancer Overview

AI / LLM Analyst. Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Innodata Annotation Platform and Other. Education includes Master of Business Administration, Institute of Business Management, Jadavpur University (2025) and Bachelor of Science, University of Calcutta (2021). AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and Data Collection.

Labeling Experience

AI / LLM Analyst

Text

Evaluated AI model outputs across multiple modalities, including text, conversation, and document structures, with feedback directly supporting retraining cycles. Conducted both qualitative and quantitative assessment of model outputs, such as intent classification, sentiment analysis, safety guideline adherence, and factual verification. Curated high-quality training datasets, enforced annotation consistency, and performed 1,000+ bounding box annotation tasks on complex document layouts. • Processed preference ranking (RLHF) and model output evaluation for large language models. • Maintained 90–95%+ accuracy on 50+ daily labeling and evaluation tasks. • Participated in guideline reviews, calibration sessions, and error escalation for iterative improvement. • Collaborated with data scientists and engineering teams to refine annotation protocols.

2025 - Present

Data Quality & Validation Intern

OtherTextData Collection

Cleaned, validated, and transformed over 200,000 structured content records using Python to ensure data accuracy and schema compliance for large-scale AI model training. Conducted exploratory data analysis and created regex-based validation processes to detect anomalies and ensure quality. Documented findings to help refine analytical and validation workflows relevant to data preparation for AI use cases. • Applied systematic data-cleaning approaches central to dataset preparation. • Used Python (Pandas, NumPy) for large-scale content processing. • Contributed to overall process improvement for analytical reproducibility. • Supported the development of robust, high-quality AI-ready datasets.

2025 - 2025

Education

Work History