Yeluru Nitish

Key Skills

Software

Google Cloud Vertex AI

Labelbox

CVAT

SuperAnnotate

AWS SageMaker

Top Subject Matter

AI Solutions Evaluation

LLM Output

Multi-step Reasoning

Top Data Types

Computer Code Programming

Text

Top Task Types

Computer Programming/Coding

Text Summarization

Freelancer Overview

Founding Full Stack Engineer (AI Evaluation & Front-End Systems). Brings 4+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes Bachelor of Technology, Baba Institute of Technology and Sciences (2025). AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and Text Summarization.

Labeling Experience

Computer Vision Dataset – Object Detection & Classification

CVATImageBounding BoxPolygon

Performed detailed object detection and classification annotations for computer vision datasets (20K+ images). Used bounding boxes and segmentation polygons to identify objects and environmental features. Ensured data uniformity through QA validation cycles and version-controlled dataset delivery for model retraining.

Performed detailed object detection and classification annotations for computer vision datasets (20K+ images). Used bounding boxes and segmentation polygons to identify objects and environmental features. Ensured data uniformity through QA validation cycles and version-controlled dataset delivery for model retraining.

2023 - 2024

AI-Integrated Web Platform – Data Workflow Management

Aws SagemakerTextComputer Programming CodingData Collection

Developed and maintained a full stack web platform integrating AI-based data pipelines for labeling workflow automation. Designed APIs to fetch, store, and visualize labeled datasets. Supported LLM fine-tuning data management, tracking label performance and feedback scores.

Developed and maintained a full stack web platform integrating AI-based data pipelines for labeling workflow automation. Designed APIs to fetch, store, and visualize labeled datasets. Supported LLM fine-tuning data management, tracking label performance and feedback scores.

2024

Conversational AI – Red Labelling & LLM Evaluation

LabelboxTextRLHFEvaluation Rating

Labeled and evaluated AI-generated responses for tone, factuality, and ethical compliance under Red Labelling standards. Created and rated prompt-response pairs for LLM fine-tuning and reinforcement learning workflows. Contributed to improving AI model reasoning, accuracy, and contextual understanding. Maintained a 98% accuracy rate with strict QA protocols.

Labeled and evaluated AI-generated responses for tone, factuality, and ethical compliance under Red Labelling standards. Created and rated prompt-response pairs for LLM fine-tuning and reinforcement learning workflows. Contributed to improving AI model reasoning, accuracy, and contextual understanding. Maintained a 98% accuracy rate with strict QA protocols.

2023 - Present

NLP Dataset – Sentiment & Entity Annotation

SuperannotateTextEntity Ner ClassificationClassification

Annotated thousands of customer text samples for named entity recognition and sentiment classification. Tagged entities (names, products, locations) and labeled sentiment/emotion categories. Enhanced dataset structure and improved downstream model accuracy by maintaining annotation consistency and balanced label distribution.

Annotated thousands of customer text samples for named entity recognition and sentiment classification. Tagged entities (names, products, locations) and labeled sentiment/emotion categories. Enhanced dataset structure and improved downstream model accuracy by maintaining annotation consistency and balanced label distribution.

2024 - 2024

Education

Work History