Ranjith Kumar J.

Key Skills

Software

Other

Remotasks

Appen

Clickworker

Data Annotation Tech

Telus

Toloka

Top Subject Matter

Machine learning

RLHF

recommendation systems

Top Data Types

Text

Document

Computer Code Programming

Top Task Types

Fine-tuning

Emotion Recognition

RLHF

Classification

Text Generation

Function Calling

Computer Programming/Coding

Question Answering

Text Summarization

Freelancer Overview

Machine Learning Intern (AICTE Idea Labs CBIT - R&D). Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other. Education includes Bachelor of Engineering, Chaitanya Bharathi Institute of Technology (2025). AI-training focus includes data types such as Computer Code, Programming, and Text and labeling workflows including Fine-tuning, Emotion Recognition, and RLHF.

Labeling Experience

AI trainer

DocumentRLHF

i have trained AI in outlier AI platform.

2024 - 2025

AI Reading Assistant (Gap Inc. Summer Internship – hackathon project)

OtherRLHF

Contributed to an AI Reading Assistant application as part of a hackathon project, involving AI/ML feature development. The work focused on creating an assistant capability for use within an enterprise application context. This was an AI training/ML development effort rather than general web engineering. • Developed AI Reading Assistant for the FDM application • Participated in Gap-Hackathon to deliver an AI-enabled feature • Implemented assistant functionality using AI/ML concepts • Coordinated project progression through documentation and roadmaps

2024 - 2024

Digital Operations Intern (WhatsApp Chat Sentiment Analyzer)

OtherTextEmotion Recognition

Built a WhatsApp chat sentiment analysis tool for group conversations using traditional ML classifiers and NLP preprocessing. The task involved training/evaluating models to classify sentiment categories and producing interpretable outputs in an interactive dashboard. Results were reported with classification accuracy and visualization of communication patterns. • Used Naive Bayes and SVM for sentiment classification • Applied NLTK for text processing • Trained/evaluated sentiment models and achieved up to 93% accuracy • Visualized patterns via an interactive Streamlit dashboard

2023 - 2023

Machine Learning Intern (AICTE Idea Labs CBIT - R&D)

OtherFine-tuning

Worked on an AI/ML internship project requiring model development with clustering and recommender-style techniques using Python and NLP. The work focused on building analytical systems (e.g., clustering and collaborative filtering) and evaluating performance metrics. Responsibilities also included non-ML planning elements under the AICTE Idea Labs program. • Implemented K-Means clustering and collaborative filtering • Applied NLP techniques including Market Basket Analysis • Achieved 83% accuracy for the project outcome • Supported project planning across marketing, sales, and finance aspects

2023 - 2023

Education

Work History