Vinoth Kumar

Key Skills

Software

AWS SageMaker

Img Lab

Labelbox

LabelImg

Label Studio

Remotasks

Scale AI

Snorkel AI

Surge AI

Other

SuperAnnotate

Top Subject Matter

Software Engineering

Coding Domain Expertise

Code Evaluation

Top Data Types

Computer Code Programming

Image

Text

Top Task Types

Computer Programming Coding

Data Collection

Fine Tuning

Prompt Response Writing SFT

RLHF

Freelancer Overview

Coding LLM Trainer / AI Data Annotator, Alignerr. Core strengths include Other and SuperAnnotate. AI-training focus includes data types such as Computer Code and Programming and labeling workflows including Computer Programming and Coding.

Reviews (1)

Labeling Experience

Expert Data Labeling Specialist for High-Quality AI Training Data

Scale AIComputer Code ProgrammingBounding BoxPolygon

Project Highlight: Automated Image Annotation for Autonomous Driving Systems Objective: To create a high-quality annotated dataset of traffic scenes for training an autonomous driving AI model. Scope: Labeled 50,000+ images with various traffic elements, including vehicles, pedestrians, traffic signs, and road markings. Developed a semi-automated annotation tool to speed up the labeling process and ensure consistency. Implemented quality control measures to validate and correct annotations, achieving an accuracy rate of over 98%. Skills Utilized: Proficient use of annotation tools such as Labelbox, CVAT, and custom-built solutions. Strong understanding of image recognition and object detection principles. Expertise in Python and JavaScript to automate repetitive tasks and enhance the labeling workflow. Collaboration with data scientists and AI engineers to ensure the dataset met the specific requirements for model training.

Project Highlight: Automated Image Annotation for Autonomous Driving Systems Objective: To create a high-quality annotated dataset of traffic scenes for training an autonomous driving AI model. Scope: Labeled 50,000+ images with various traffic elements, including vehicles, pedestrians, traffic signs, and road markings. Developed a semi-automated annotation tool to speed up the labeling process and ensure consistency. Implemented quality control measures to validate and correct annotations, achieving an accuracy rate of over 98%. Skills Utilized: Proficient use of annotation tools such as Labelbox, CVAT, and custom-built solutions. Strong understanding of image recognition and object detection principles. Expertise in Python and JavaScript to automate repetitive tasks and enhance the labeling workflow. Collaboration with data scientists and AI engineers to ensure the dataset met the specific requirements for model training.

2022 - 2024

LLM Trainer / AI Data Annotator, SuperAnnotate

Superannotate

In my role at SuperAnnotate as an LLM Trainer and AI Data Annotator, I participated in the fine-tuning and evaluation of large language models through in-depth coding solution reviews. I curated and annotated high-quality datasets focusing on complex programming tasks and logical reasoning workflows. My feedback ensured alignment with software development best practices and improved instruction-following techniques for AI systems. • Collaborated with QA and research teams to refine annotation protocols. • Identified edge cases and contributed to guideline iteration. • Enhanced dataset quality for model training and evaluation. • Applied software engineering expertise to annotation processes.

In my role at SuperAnnotate as an LLM Trainer and AI Data Annotator, I participated in the fine-tuning and evaluation of large language models through in-depth coding solution reviews. I curated and annotated high-quality datasets focusing on complex programming tasks and logical reasoning workflows. My feedback ensured alignment with software development best practices and improved instruction-following techniques for AI systems. • Collaborated with QA and research teams to refine annotation protocols. • Identified edge cases and contributed to guideline iteration. • Enhanced dataset quality for model training and evaluation. • Applied software engineering expertise to annotation processes.

2025 - Present

Coding LLM Trainer / AI Data Annotator, Alignerr

Other

As a Coding LLM Trainer and AI Data Annotator at Alignerr, I reviewed and enhanced AI-generated code to adhere to expert-level programming standards. I meticulously annotated code tasks and logical problem-solving prompts to facilitate the fine-tuning of large language models. Ensuring accuracy and consistency, I provided structured feedback on diverse coding outputs to improve instruction-following and coherence. • Performed quality assurance checks on data labeling workflows. • Evaluated multi-step coding solutions and reasoning chains. • Enhanced alignment between AI outputs and expert developer practice. • Collaborated with teams to refine annotation guidelines.

As a Coding LLM Trainer and AI Data Annotator at Alignerr, I reviewed and enhanced AI-generated code to adhere to expert-level programming standards. I meticulously annotated code tasks and logical problem-solving prompts to facilitate the fine-tuning of large language models. Ensuring accuracy and consistency, I provided structured feedback on diverse coding outputs to improve instruction-following and coherence. • Performed quality assurance checks on data labeling workflows. • Evaluated multi-step coding solutions and reasoning chains. • Enhanced alignment between AI outputs and expert developer practice. • Collaborated with teams to refine annotation guidelines.

2025 - Present

Coding LLM Trainer, Outlier.ai

Other

As a Coding LLM Trainer at Outlier.ai, I delivered expert reviews and evaluations for AI-generated programming solutions across multiple languages. I annotated complex programming tasks and debugging scenarios to strengthen model reasoning and developer output alignment. I simulated realistic problem-solving strategies for AI training and collaborated on benchmarks for prompt and code quality. • Provided in-depth feedback for reinforcement learning fine-tuning. • Worked directly with AI researchers on data quality improvement. • Developed guidelines for code correctness and alignment. • Supported LLMs alignment with professional coding standards.

As a Coding LLM Trainer at Outlier.ai, I delivered expert reviews and evaluations for AI-generated programming solutions across multiple languages. I annotated complex programming tasks and debugging scenarios to strengthen model reasoning and developer output alignment. I simulated realistic problem-solving strategies for AI training and collaborated on benchmarks for prompt and code quality. • Provided in-depth feedback for reinforcement learning fine-tuning. • Worked directly with AI researchers on data quality improvement. • Developed guidelines for code correctness and alignment. • Supported LLMs alignment with professional coding standards.

2024 - Present

Education

Work History