Khaleelu R.

Key Skills

Software

Labelbox

Appen

Lionbridge

OneForma

Micro1

CVAT

Roboflow

Label Studio

Top Subject Matter

Computer Vision (Face Recognition) and NLP (Intent/Entity Tagging)

General data annotation and AI evaluation

Legal Services & Contract Review

Top Data Types

Image

Text

Document

Video

Top Task Types

Bounding Box

Entity (NER) Classification

Classification

Text Generation

Text Summarization

RLHF

Fine-tuning

Transcription

Evaluation/Rating

Data Collection

Object Detection

Segmentation

Freelancer Overview

Junior AI Developer — Data labeling and annotation for image/video/text datasets. Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Labelbox, Outlier AI, and CVAT. Education includes Bachelor of Technology, Rajalakshmi Institute of Technology (2024). AI-training focus includes data types such as Image, Video, and Text and labeling workflows including Bounding Box, Segmentation, and Entity (NER) Classification.

Labeling Experience

Freelance Data Annotation Specialist

LabelboxImageBounding BoxClassification

Performed large-scale image, video, and text annotation to build datasets for machine learning and deep learning systems. Labeled face recognition data with bounding boxes, landmarks, and identity mappings, ensuring annotations align with the provided guidelines. Cleaned, validated, and structured datasets to improve model accuracy and annotation consistency across computer vision and NLP use cases. • Created bounding-box and keypoint/landmark labels for face recognition • Annotated video and image datasets for computer vision tasks • Labeled NLP datasets including intent classification • Performed dataset QA with validation and consistency checks

2024 - Present

Freelance Data Annotation Specialist

AudioTranscription

I worked on an audio transcription and annotation project using Labelbox, where the goal was to convert spoken audio into accurate text for training speech-to-text models. My work involved listening to audio files, transcribing them precisely, and ensuring correct formatting, punctuation, and speaker clarity. I also handled cases with background noise, accents, and multiple speakers, maintaining high-quality standards. In addition to transcription, I performed quality checks and corrections to ensure consistency and accuracy across the dataset. This project helped improve my attention to detail and understanding of how high-quality labeled audio data contributes to building reliable ASR (Automatic Speech Recognition) systems.

2026 - 2026

Freelance Data Annotation Specialist

TextRLHF

I worked on a Reinforcement Learning from Human Feedback (RLHF) project focused on improving the quality of LLM responses. My role involved reviewing AI-generated outputs, comparing multiple responses, and ranking them based on factors like relevance, accuracy, clarity, and safety. I also provided detailed feedback to help fine-tune model behavior and reduce issues like hallucination or ambiguity. The project included tasks such as response evaluation, preference ranking, prompt-response analysis, and quality validation, ensuring the model aligns better with human expectations. This experience helped me develop strong skills in AI evaluation, annotation, and understanding model behavior, especially in real-world conversational use cases.

2025 - 2026

Education

Work History