Saipureddy Sekhar

Key Skills

Software

AWS SageMaker

Appen

Axiom AI

Clickworker

CloudFactory

CrowdSource

CVAT

Data Annotation Tech

Dataloop

Google Cloud Vertex AI

Labelbox

LabelImg

Label Studio

Mindrift

OneForma

OpenCV AI Kit (OAK)

Prodigy

Remotasks

Scale AI

SuperAnnotate

Surge AI

Toloka

Telus

VoTT

V7 Labs

Internal/Proprietary Tooling

Top Subject Matter

Healthcare and Medical Data

Administrative and Customer Support

E-commerce and Retail

Top Data Types

Audio

Computer Code Programming

Text

Top Label Types

Audio Recording

Computer Programming Coding

Data Collection

Geocoding

Mapping

Freelancer Overview

I bring a detail-oriented approach to data labeling and AI training, leveraging my background in administrative support, customer service, and the healthcare sector. My experience includes annotating data for machine learning models, particularly in text and image-based tasks. I am skilled in using various labeling platforms, such as Labelbox and Super Annotate, to accurately classify and tag data for applications like natural language processing, sentiment analysis, and object detection. I have worked with healthcare data, such as dental and medical images, and supported AI models in customer service roles by labeling chat logs and emails to improve automated responses. My ability to maintain data quality and consistency while adhering to project guidelines sets me apart, ensuring that AI models are trained on high-quality, accurate data.

Labeling Experience

Mathematics QA / AI Trainer

Data Annotation TechImageBounding BoxPolygon

As a Mathematics QA / AI Trainer, I review, evaluate, and enhance AI-generated mathematical content to ensure accuracy, clarity, and logical consistency. The role involves analyzing math prompts and model responses, verifying step-by-step reasoning, identifying computational or conceptual errors, and providing detailed feedback to improve AI model performance. I also develop grading rubrics, design improved prompts, and write high-quality, step-by-step solutions aligned with educational and problem-solving standards. Using Python (NumPy, SymPy, pandas), I automate data validation and solution verification processes. The position demands strong expertise in mathematical reasoning, familiarity with LLM (Large Language Model) behavior, and the ability to communicate technical insights effectively to both engineers and content teams.

As a Mathematics QA / AI Trainer, I review, evaluate, and enhance AI-generated mathematical content to ensure accuracy, clarity, and logical consistency. The role involves analyzing math prompts and model responses, verifying step-by-step reasoning, identifying computational or conceptual errors, and providing detailed feedback to improve AI model performance. I also develop grading rubrics, design improved prompts, and write high-quality, step-by-step solutions aligned with educational and problem-solving standards. Using Python (NumPy, SymPy, pandas), I automate data validation and solution verification processes. The position demands strong expertise in mathematical reasoning, familiarity with LLM (Large Language Model) behavior, and the ability to communicate technical insights effectively to both engineers and content teams.

2022

AI Programming & Data Labeling Specialist

LabelboxImageBounding BoxPolygon

Worked on multiple AI/ML data labeling projects involving both structured and unstructured data. Tasks included annotating images using bounding boxes and polygons for object detection and segmentation, tagging and classifying text for NLP applications (e.g., NER, sentiment analysis, summarization), and creating high-quality prompt-response pairs for fine-tuning large language models. Also scripted automation pipelines in Python to preprocess datasets and validate annotations. Ensured high accuracy through inter-annotator agreement reviews, multi-stage QA, and tool-specific validations. Contributed to both pre-training and fine-tuning dataset preparation across industries including healthcare diagnostics, autonomous driving, and generative AI applications.

Worked on multiple AI/ML data labeling projects involving both structured and unstructured data. Tasks included annotating images using bounding boxes and polygons for object detection and segmentation, tagging and classifying text for NLP applications (e.g., NER, sentiment analysis, summarization), and creating high-quality prompt-response pairs for fine-tuning large language models. Also scripted automation pipelines in Python to preprocess datasets and validate annotations. Ensured high accuracy through inter-annotator agreement reviews, multi-stage QA, and tool-specific validations. Contributed to both pre-training and fine-tuning dataset preparation across industries including healthcare diagnostics, autonomous driving, and generative AI applications.

2022

Custom Labeling Script Engineer (Python/XML/JSON)

CVATVideoBounding BoxEntity Ner Classification

Developed custom data labeling scripts using Python for large-scale annotation projects across diverse data types, including image (bounding boxes/segmentation), text (NER, classification), and audio (transcription alignment). Used XML and JSON formats for label schema creation, data conversion, and tool integration. Engineered validation scripts to enforce labeling guidelines and maintain data consistency. Automated annotation pre-processing (e.g., image resizing, token parsing), batch uploads, and QA pipelines. Collaborated with ML teams to generate structured datasets for model training, fine-tuning (SFT), and evaluation. Delivered high-volume annotations (>250k instances) with >98% accuracy across multiple AI domains.

Developed custom data labeling scripts using Python for large-scale annotation projects across diverse data types, including image (bounding boxes/segmentation), text (NER, classification), and audio (transcription alignment). Used XML and JSON formats for label schema creation, data conversion, and tool integration. Engineered validation scripts to enforce labeling guidelines and maintain data consistency. Automated annotation pre-processing (e.g., image resizing, token parsing), batch uploads, and QA pipelines. Collaborated with ML teams to generate structured datasets for model training, fine-tuning (SFT), and evaluation. Delivered high-volume annotations (>250k instances) with >98% accuracy across multiple AI domains.

2023 - 2024

Healthcare Data Annotation for Medical Imaging AI Models

LabelboxImageBounding BoxEntity Ner Classification

In this project, I worked on annotating medical images, including X-rays and dental scans, to support the development of AI models for diagnosis and treatment planning in orthodontics. Tasks included drawing bounding boxes around key dental structures and segmenting areas for more granular analysis. Additionally, I labeled patient records and text data using Named Entity Recognition (NER) for automated processing of healthcare documentation. The project spanned over 5,000 images and numerous text records, with a focus on maintaining a high accuracy rate of over 95% through regular quality checks and reviews.

In this project, I worked on annotating medical images, including X-rays and dental scans, to support the development of AI models for diagnosis and treatment planning in orthodontics. Tasks included drawing bounding boxes around key dental structures and segmenting areas for more granular analysis. Additionally, I labeled patient records and text data using Named Entity Recognition (NER) for automated processing of healthcare documentation. The project spanned over 5,000 images and numerous text records, with a focus on maintaining a high accuracy rate of over 95% through regular quality checks and reviews.

2023 - 2024

Education

Work History