Aditya K.

Key Skills

Software

Label Studio

Other

CVAT

Don't disclose

Top Subject Matter

Multi-modal AI training data annotation and agent workflow quality assurance

AI instruction-following evaluation and training-data annotation support

Top Data Types

Video

Image

Text

Document

Top Task Types

Segmentation

Classification

Action Recognition

Bounding Box

Object Detection

Prompt + Response Writing (SFT)

Data Collection

Freelancer Overview

AI Trainer & Data Annotation Specialist (Outlier AI). Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other and Don't disclose. Education includes Bachelor of Technology, Rungta College of Engineering and Technology (2026) and Higher Secondary Certificate, RLSY College (2022). AI-training focus includes data types such as Text, Image, and Computer Code and labeling workflows including Evaluation, Rating, and Prompt + Response Writing (SFT).

Labeling Experience

AI Trainer (Aether Generalist) | Outlier AI | Freelance, Remote

OtherTextObject DetectionData Collection

Served as an AI Trainer and Data Annotator processing multimodal dataset items using computer vision annotation workflows. Maintained a 95%+ accuracy rate through systematic quality checks and cross-source consistency validation. Documented error and edge cases to support iterative pipeline improvement and consistent labeling standards across the dataset. • Annotated and structured 150+ items/week using CVAT for image/video/multimodal content. • Performed verification steps including formatting validation and edge-case documentation before delivery. • Collaborated with QA specialists to meet daily throughput targets in an independent remote environment. • Applied Python-based data tooling to build and validate structured datasets used for downstream extraction workflows.

2025 - Present

Freelance Content Writer & AI Training Contributor

Don't discloseTextPrompt + Response Writing (SFT)Data Collection

Created structured AI training datasets focused on supervised learning style prompt-response examples. Evaluated AI-generated text for reasoning accuracy, logical coherence, and clarity, providing detailed feedback for iteration. Performed research and fact-checking to ensure factual correctness prior to dataset submission. • Built prompt-response pairs, reasoning chains, and instruction-following examples with strict formatting and quality standards. • Conducted quality evaluation of generated responses and provided feedback for model improvement. • Verified information accuracy through thorough research and fact-checking before submission. • Adapted output style to technical, academic, and professional audience requirements per project specifications.

2024 - Present

Image Annotation Practice Project

CVATImageClassificationBounding Box

In this project, I independently constructed an end-to-end dataset for object detection using CVAT and Python. I applied bounding boxes, polygons, and keypoint labels for over 500 images, and enforced rigorous standards for label consistency and inter-annotator agreement. Self-QA reviews were performed to minimize errors and simulate professional annotation workflows. • Developed structured annotations for object detection and segmentation tasks • Utilized CVAT and Python to label images with bounding boxes, polygons, and keypoints • Emphasized quality control through self-review to reduce errors • Replicated throughput and accuracy expected in production-level annotation pipelines.

2024 - Present

Video Annotation Practice Project (Self-Learning)

Label StudioVideoSegmentationAction Recognition

In this self-learning project, I performed video annotation for AI training using Label Studio. I focused on identifying relevant moments and accurately marking temporal boundaries. Actions and visible objects were classified according to instructions. Metadata was added to enhance dataset usability. • Annotated short videos for AI training. • Identified and marked accurate temporal boundaries. • Classified actions and objects based on instructions. • Tagged metadata to improve dataset quality.

2022 - Present

Education

Work History