Gopi A

Key Skills

Software

Scale AI

Labelbox

CVAT

iMerit

Top Subject Matter

Computer Vision

Generative AI

LLM Prompt Evaluation

Top Data Types

Image

Text

Top Task Types

Bounding Box

RLHF

Freelancer Overview

Independent Data Annotator. Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Scale AI, Labelbox, and CVAT. Education includes Master of Business Administration, Pondicherry University (2023) and Bachelor of Engineering, Visvesvaraya Technological University (2023). AI-training focus includes data types such as Image and Text and labeling workflows including Bounding Box, RLHF, and Evaluation.

Labeling Experience

Independent Data Annotator

Scale AIImageBounding Box

Executed high-precision 2D/3D bounding box, polygon segmentation, and key point image annotation to train computer vision models. Authored creative and adversarial text prompts for generative model stress testing and evaluation. Evaluated and rated AI-generated text prompts and responses for factual accuracy, safety, and helpfulness using RLHF methodologies. • Labeled over 50,000 images for object detection and segmentation tasks. • Maintained a consistent 99% QA acceptance rate and adhered to evolving guidelines. • Independently managed large dataset batches and client timelines. • Authored and evaluated over 10,000 AI-generated prompts and responses for LLM alignment.

Executed high-precision 2D/3D bounding box, polygon segmentation, and key point image annotation to train computer vision models. Authored creative and adversarial text prompts for generative model stress testing and evaluation. Evaluated and rated AI-generated text prompts and responses for factual accuracy, safety, and helpfulness using RLHF methodologies. • Labeled over 50,000 images for object detection and segmentation tasks. • Maintained a consistent 99% QA acceptance rate and adhered to evolving guidelines. • Independently managed large dataset batches and client timelines. • Authored and evaluated over 10,000 AI-generated prompts and responses for LLM alignment.

2025 - Present

Junior Data Annotator

ImageBounding Box

Accurately labeled, classified, and structured large volumes of raw text and image data for machine learning training sets across various domains. Reviewed completed batches, ensuring a strict 98%+ accuracy threshold before final submission. Evaluated AI-generated text responses with RLHF and performed named entity recognition, semantic segmentation, and bounding box annotation on complex datasets. • Labeled over 35,000 images and large amounts of text data. • Flagged edge cases and contributed to clarification of ambiguous guidelines. • Engaged in QA to maintain high standards of labeling accuracy. • Communicated directly with project managers to improve labeling workflows.

Accurately labeled, classified, and structured large volumes of raw text and image data for machine learning training sets across various domains. Reviewed completed batches, ensuring a strict 98%+ accuracy threshold before final submission. Evaluated AI-generated text responses with RLHF and performed named entity recognition, semantic segmentation, and bounding box annotation on complex datasets. • Labeled over 35,000 images and large amounts of text data. • Flagged edge cases and contributed to clarification of ambiguous guidelines. • Engaged in QA to maintain high standards of labeling accuracy. • Communicated directly with project managers to improve labeling workflows.

2024 - 2025

AI Image Quality Evaluation (TELUS International)

Image

Executed side-by-side quality evaluations on over 20,000 AI-generated images for computer vision model alignment. Systematically analyzed visual fidelity, artifact rates, and image-to-prompt correspondence to ensure high-quality outputs. Maintained a 99% QA accuracy score and contributed to quality control for computer vision applications. • Categorized rendering errors and artifacts in AI-generated images. • Directly supported fine-tuning of generative computer vision models. • Helped document quality issues for continual AI model improvement. • Utilized internal and external tools for large-scale visual quality assessment.

Executed side-by-side quality evaluations on over 20,000 AI-generated images for computer vision model alignment. Systematically analyzed visual fidelity, artifact rates, and image-to-prompt correspondence to ensure high-quality outputs. Maintained a 99% QA accuracy score and contributed to quality control for computer vision applications. • Categorized rendering errors and artifacts in AI-generated images. • Directly supported fine-tuning of generative computer vision models. • Helped document quality issues for continual AI model improvement. • Utilized internal and external tools for large-scale visual quality assessment.

Not specified

Generative AI Prompt Evaluation (Project Callisto)

TextRLHF

Evaluated over 15,000 prompt-response pairs to support LLM fine-tuning in generative AI projects. Assessed factual accuracy, safety, and tone per RLHF guidelines, documenting data anomalies to improve alignment. Authored multi-turn adversarial prompts to identify hallucination triggers in model outputs for improved robustness. • Applied consistent evaluation processes for prompt-based generative tasks. • Ensured strict adherence to RLHF rating protocols and safety considerations. • Improved LLM outcomes by isolating alignment gaps and capturing anomalies. • Collaborated with project teams to enhance data quality and documentation.

Evaluated over 15,000 prompt-response pairs to support LLM fine-tuning in generative AI projects. Assessed factual accuracy, safety, and tone per RLHF guidelines, documenting data anomalies to improve alignment. Authored multi-turn adversarial prompts to identify hallucination triggers in model outputs for improved robustness. • Applied consistent evaluation processes for prompt-based generative tasks. • Ensured strict adherence to RLHF rating protocols and safety considerations. • Improved LLM outcomes by isolating alignment gaps and capturing anomalies. • Collaborated with project teams to enhance data quality and documentation.

Not specified

Computer Vision Annotation Project (iMerit)

ImeritImageBounding Box

Annotated over 20,000 image frames of traffic, humans, and animals for autonomous vehicle perception and biomechanical tracking projects. Performed 2D bounding box, polygon segmentation, and key point pose estimation tasks with high accuracy. Managed data labeling complexities and resolved edge case scenarios for model precision improvement. • Used CVAT to achieve a 98.5% precision rate in annotation. • Supported both autonomous vehicle and human/animal biomechanics domains. • Addressed challenging frames, ensuring data alignment with project goals. • Facilitated algorithm training with gold-standard labeled datasets.

Annotated over 20,000 image frames of traffic, humans, and animals for autonomous vehicle perception and biomechanical tracking projects. Performed 2D bounding box, polygon segmentation, and key point pose estimation tasks with high accuracy. Managed data labeling complexities and resolved edge case scenarios for model precision improvement. • Used CVAT to achieve a 98.5% precision rate in annotation. • Supported both autonomous vehicle and human/animal biomechanics domains. • Addressed challenging frames, ensuring data alignment with project goals. • Facilitated algorithm training with gold-standard labeled datasets.

Not specified

Education

Work History