Key Skills

Software

AWS SageMaker

Anno-Mage

Appen

Axiom AI

Argilla

Clickworker

CloudFactory

CrowdFlower

CrowdSource

Data Annotation Tech

CVAT

Dataloop

Datatroniq

Datumbox

Datasaur

Datature

Dataturk

Deep Systems

Diffgram

Doccano

Encord

Figure Eight

Google Cloud Vertex AI

Hasty

HiveMind

Humanatic

iMerit

Img Lab

Kili Technology

Labelbox

LabelImg

Label Studio

LightTag

Lionbridge

Mercor

Mighty AI

Mindrift

OneForma

OpenCV AI Kit (OAK)

Playment

Prodigy

Remotasks

Roboflow

Sama

Scale AI

Sloth

Snorkel AI

SuperAnnotate

Supervisely

Surge AI

Top Subject Matter

Financial data

Fraud/Risk Analysis

Aml Domain Expertise

Top Data Types

Text

Document

Image

Top Task Types

Classification

Bounding Box

Segmentation

Polygon

Point/Key Point

Entity (NER) Classification

Cuboid

Text Generation

Text Summarization

Fine-tuning

Transcription

Red Teaming

Computer Programming/Coding

Question Answering

RLHF

Polyline

Evaluation/Rating

Object Detection

Data Collection

Function Calling

Prompt + Response Writing (SFT)

Freelancer Overview

Software engineer with 5 years of experience working with large-scale structured datasets at Amazon and in my own projects. While my background is in engineering, much of my work has involved tasks that map directly to data labeling: designing classification schemas, validating data quality across hundreds of thousands of records, investigating edge cases and labeling inconsistencies, and building pipelines that surface clean signals from noisy data. At Amazon, I resolved a pagination bug that was silently truncating 200,000 records, built an enrichment pipeline reconciling HR hierarchy data across 30,000+ entries, and diagnosed a false-success error pipeline. All of this required precision, consistency, and careful attention to data boundaries. I also designed an in-house risk classification service for my startup BillingBase, where I built and tuned heuristics for transaction risk scoring, AML screening, and fraud detection without third-party tools. This required defining clear labeling rules and handling ambiguous cases at the edges. My technical background lets me work directly with raw datasets, understand the downstream impact of labeling decisions, and maintain consistency across high-volume workflows. I'm a fast learner who takes quality seriously and holds myself to a high standard on repetitive, detail-oriented work.

Labeling Experience

Education

Work History