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Ritwick Mondal

Ritwick Mondal

LLM trainer in bengali,hindi and english,machine learning,deep learning

India flagN/A, India
$20.00/hrIntermediateOther

Key Skills

Software

Other

Top Subject Matter

Conversational AI
LLM Evaluation
Prompt Engineering

Top Data Types

TextText
DocumentDocument
ImageImage

Top Task Types

Prompt + Response Writing (SFT)Prompt + Response Writing (SFT)

Freelancer Overview

AI Intern – LLM Chat Application Bringing hands-on experience in building and optimizing LLM-powered systems, with a strong focus on scalable and efficient AI workflows. Currently developing a chat application using Model Context Protocol (MCP), emphasizing latency optimization, response quality, and user experience. Demonstrates expertise across complex AI pipelines, including data processing, feature engineering, and model evaluation, supported by experience at DRDO (CAIR Lab) working on encrypted traffic classification using advanced ML techniques and interpretability methods. Core strengths include working with internal and proprietary tooling, Generative AI systems, and structured data workflows. Experienced in AI training processes involving text data, prompt engineering, and supervised fine-tuning (SFT) through prompt-response design. Education: Bachelor of Technology in Computer Science and Engineering, National Institute of Technology, Durgapur (2022–June 2026)

IntermediateEnglish

Labeling Experience

AI Intern - LLM Chat Application

TextPrompt Response Writing SFT
Worked on a large language model (LLM) powered chat application, focusing on response optimization and user experience. Participated in training LLMs with prompt engineering and user interaction data. Applied and evaluated various approaches to contextual response generation to enhance AI system behavior. • Designed prompts and user input/output pairs for data collection. • Evaluated LLM responses for accuracy, relevance, and safety. • Provided feedback and ratings to fine-tune the LLM's conversational abilities. • Collaborated with a team to improve LLM latency and scalability.

Worked on a large language model (LLM) powered chat application, focusing on response optimization and user experience. Participated in training LLMs with prompt engineering and user interaction data. Applied and evaluated various approaches to contextual response generation to enhance AI system behavior. • Designed prompts and user input/output pairs for data collection. • Evaluated LLM responses for accuracy, relevance, and safety. • Provided feedback and ratings to fine-tune the LLM's conversational abilities. • Collaborated with a team to improve LLM latency and scalability.

2026 - Present

Research Intern

TextData Collection
Encrypted Instant Messaging Traffic Classification System (DRDO – CAIR Lab) Developed an end-to-end machine learning pipeline for classifying encrypted instant messaging (IM) traffic using network flow data. Built a custom PCAP dataset and performed flow-level feature extraction using Tranalyzer2, followed by rigorous feature selection techniques including Pearson correlation, Boruta, and RFECV. Implemented and compared multiple ML models such as LightGBM, CatBoost, XGBoost, and SVM, evaluating performance using F1-score, balanced accuracy, and runtime efficiency. Integrated SHAP-based explainability to interpret model decisions and enhance transparency. The system enables accurate classification of encrypted traffic without payload inspection, making it suitable for privacy-preserving network monitoring and cybersecurity applications.

Encrypted Instant Messaging Traffic Classification System (DRDO – CAIR Lab) Developed an end-to-end machine learning pipeline for classifying encrypted instant messaging (IM) traffic using network flow data. Built a custom PCAP dataset and performed flow-level feature extraction using Tranalyzer2, followed by rigorous feature selection techniques including Pearson correlation, Boruta, and RFECV. Implemented and compared multiple ML models such as LightGBM, CatBoost, XGBoost, and SVM, evaluating performance using F1-score, balanced accuracy, and runtime efficiency. Integrated SHAP-based explainability to interpret model decisions and enhance transparency. The system enables accurate classification of encrypted traffic without payload inspection, making it suitable for privacy-preserving network monitoring and cybersecurity applications.

2025 - 2025

Education

N

National Institute of Technology, Durgapur

Bachelor of Technology, Computer Science and Engineering

Bachelor of Technology
2022

Work History

O

OpenLM

AI Intern

Kolkata
2026 - Present
D

DRDO

Software Engineering Intern

Bengaluru
2025 - 2025