Sypureddy Sekhar

Key Skills

Software

AWS SageMaker

Appen

Axiom AI

Clickworker

CrowdFlower

CrowdSource

CVAT

Data Annotation Tech

Dataloop

Datature

HiveMind

iMerit

Labelbox

LightTag

Lionbridge

Mindrift

OneForma

Prodigy

Remotasks

Scale AI

SuperAnnotate

Surge AI

Toloka

Telus

V7 Labs

Internal/Proprietary Tooling

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Computer Code Programming

Image

Top Task Types

Audio Recording

Classification

Computer Programming/Coding

Data Collection

RLHF

Freelancer Overview

I have hands-on experience in data labeling and AI training, with a strong focus on quality, consistency, and attention to detail. My background includes annotating large datasets for machine learning models across various industries, including e-commerce, customer service, and transportation. I have worked on tasks like image classification, bounding box annotation, content moderation, sentiment analysis, and chatbot response evaluation. I'm familiar with common annotation platforms, such as Labelbox and Scale AI, as well as proprietary tools, and I’m comfortable adapting to new guidelines quickly. What sets me apart is my ability to understand project-specific requirements and maintain high accuracy while meeting deadlines. I have also collaborated with QA teams to ensure alignment with gold standards and have participated in feedback loops to improve annotation consistency. My strong communication skills, multilingual capability (if applicable), and disciplined approach to data quality make me a reliable contributor to AI training projects.

Labeling Experience

Image Annotation for Self-Driving Car Data

CVATImageBounding BoxPolygon

I participated in a large-scale image annotation project for self-driving car training datasets through Remo tasks. The task involved accurately labeling thousands of street-level images by identifying and drawing bounding boxes, polygons, and polylines around road elements such as vehicles, pedestrians, bicycles, lane lines, and traffic signs. I ensured high precision in object detection and classification, following strict task guidelines and quality assurance feedback. My annotations directly contributed to training and improving computer vision models for autonomous navigation and obstacle detection. I consistently maintained high-quality scores and met daily productivity targets while adapting to evolving task instructions.

I participated in a large-scale image annotation project for self-driving car training datasets through Remo tasks. The task involved accurately labeling thousands of street-level images by identifying and drawing bounding boxes, polygons, and polylines around road elements such as vehicles, pedestrians, bicycles, lane lines, and traffic signs. I ensured high precision in object detection and classification, following strict task guidelines and quality assurance feedback. My annotations directly contributed to training and improving computer vision models for autonomous navigation and obstacle detection. I consistently maintained high-quality scores and met daily productivity targets while adapting to evolving task instructions.

2016 - 2018

Image Quality Classification

Scale AIImageClassificationEvaluation Rating

I participated in a project focused on evaluating and classifying image quality to support data preprocessing for computer vision models. Tasks included identifying visual issues such as blurriness, lighting problems, cropping errors, and obstruction of key subjects. Each image was evaluated and labeled based on predefined criteria such as clarity, framing, and usefulness for training. The annotations helped data engineers filter low-quality inputs and improve dataset accuracy for AI systems in sectors like retail, autonomous vehicles, and surveillance. I maintained high accuracy and consistently followed evolving quality standards and reviewer feedback.

I participated in a project focused on evaluating and classifying image quality to support data preprocessing for computer vision models. Tasks included identifying visual issues such as blurriness, lighting problems, cropping errors, and obstruction of key subjects. Each image was evaluated and labeled based on predefined criteria such as clarity, framing, and usefulness for training. The annotations helped data engineers filter low-quality inputs and improve dataset accuracy for AI systems in sectors like retail, autonomous vehicles, and surveillance. I maintained high accuracy and consistently followed evolving quality standards and reviewer feedback.

2022

Audio Transcription and Tagging

AppenAudioSegmentationClassification

In this project, I transcribed short audio clips into accurate text and tagged speech data with relevant metadata such as speaker labels, background noise, speech clarity, and emotion indicators. I also classified the tone and intent of the speakers and segmented long recordings into speaker turns. The data supported the training of automatic speech recognition (ASR) and voice assistant models. I adhered to strict linguistic and formatting guidelines, and regularly passed QA checks and benchmark tests to ensure consistent annotation quality. My work contributed to improving voice-enabled AI systems used in customer service and smart assistants.

In this project, I transcribed short audio clips into accurate text and tagged speech data with relevant metadata such as speaker labels, background noise, speech clarity, and emotion indicators. I also classified the tone and intent of the speakers and segmented long recordings into speaker turns. The data supported the training of automatic speech recognition (ASR) and voice assistant models. I adhered to strict linguistic and formatting guidelines, and regularly passed QA checks and benchmark tests to ensure consistent annotation quality. My work contributed to improving voice-enabled AI systems used in customer service and smart assistants.

2020

Text Categorization & Sentiment Analysis

AppenTextClassificationQuestion Answering

I worked on multiple text-based AI training projects via Appen, focusing on classifying and labeling user-generated content from platforms like social media, product reviews, and customer support chats. Tasks included identifying sentiment (positive, neutral, negative), tagging intent, and evaluating the appropriateness and tone of user statements. I followed strict labeling guidelines to ensure consistency and quality across annotations, often participating in calibration sessions and quality audits. The project involved thousands of entries, and I maintained above 95% accuracy as measured by regular QA reviews and gold-standard checks.

I worked on multiple text-based AI training projects via Appen, focusing on classifying and labeling user-generated content from platforms like social media, product reviews, and customer support chats. Tasks included identifying sentiment (positive, neutral, negative), tagging intent, and evaluating the appropriateness and tone of user statements. I followed strict labeling guidelines to ensure consistency and quality across annotations, often participating in calibration sessions and quality audits. The project involved thousands of entries, and I maintained above 95% accuracy as measured by regular QA reviews and gold-standard checks.

2019

Labelbox Automation Engineer – Python/JavaScript

LabelboxImageBounding BoxPolygon

Built automated workflows for large-scale data annotation in Labelbox using Python and JavaScript SDKs. Designed custom labeling templates, set up ontology schemas, and implemented scripts for data upload, labeling queue management, quality review, and label export in JSON format. Integrated with Labelbox Model-assisted Labeling (MAL) features to pre-label datasets using model predictions and accelerate annotation throughput. Optimized QA review processes via automated flagging and metric tracking. Annotated diverse data types (images, video, text), including bounding boxes for object detection in retail and segmentation for medical scans. Collaborated with ML engineers to refine prompt-response pairs and function-calling structures for LLM fine-tuning. Delivered over 300,000+ labeled items with automated validation pipelines ensuring >97% accuracy.

Built automated workflows for large-scale data annotation in Labelbox using Python and JavaScript SDKs. Designed custom labeling templates, set up ontology schemas, and implemented scripts for data upload, labeling queue management, quality review, and label export in JSON format. Integrated with Labelbox Model-assisted Labeling (MAL) features to pre-label datasets using model predictions and accelerate annotation throughput. Optimized QA review processes via automated flagging and metric tracking. Annotated diverse data types (images, video, text), including bounding boxes for object detection in retail and segmentation for medical scans. Collaborated with ML engineers to refine prompt-response pairs and function-calling structures for LLM fine-tuning. Delivered over 300,000+ labeled items with automated validation pipelines ensuring >97% accuracy.

2023 - 2024

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Education

Work History