Key Skills

Software

AWS SageMaker

Anno-Mage

Appen

Clickworker

Data Annotation Tech

Google Cloud Vertex AI

Remotasks

Scale AI

Snorkel AI

Telus

V7 Labs

CVAT

Supervisely

Top Subject Matter

No subject matter listed

Top Data Types

Document

Geospatial Tiled Imagery

Image

Top Task Types

Classification

Computer Programming Coding

Evaluation Rating

Prompt Response Writing SFT

Translation Localization

Freelancer Overview

With 3+ years of specialized experience in AI training and data labeling, I’ve contributed to high-impact projects across healthcare, autonomous systems, and customer service domains. My expertise in natural language processing (NLP) includes fine-tuning large language models (LLMs) for conversational AI applications, such as developing a healthcare chatbot that reduced patient query resolution time by 40% through optimized prompt engineering and bias mitigation strategies. In data labeling, I’ve led annotation initiatives for medical imaging datasets (e.g., tumor detection in radiology scans) and sensor data for autonomous vehicles, achieving 99% accuracy rates by implementing rigorous quality assurance frameworks and training annotator teams on complex guidelines. What sets me apart is my dual focus on technical precision and ethical AI practices. For example, I designed a data labeling workflow for an e-commerce client that improved product categorization efficiency by 30% while ensuring fairness in demographic representation. My ability to bridge technical execution with business needs—such as translating clinician feedback into model improvements for a diagnostic AI tool—has enabled scalable, user-centric solutions. I thrive in cross-functional environments, leveraging tools like Python, Label Studio, and AWS SageMaker to deliver end-to-end AI pipelines that align with real-world use cases.

Labeling Experience

Autonomous Vehicles: Sensor Fusion Annotation

SuperviselyImagePolygonEntity Ner Classification

Trained a team of 15 annotators on multi-sensor synchronization and edge-case scenarios (e.g., occluded objects, poor weather). Achieved 99.5% consistency in labeling across 200,000 frames, directly improving perception model recall rates. Tools: Scale AI, Supervisely, custom scripting for data validation.

Trained a team of 15 annotators on multi-sensor synchronization and edge-case scenarios (e.g., occluded objects, poor weather). Achieved 99.5% consistency in labeling across 200,000 frames, directly improving perception model recall rates. Tools: Scale AI, Supervisely, custom scripting for data validation.

2024 - 2024

Healthcare: Medical Imaging Diagnostics

CVATImageEntity Ner ClassificationSegmentation

Developed guidelines for labeling tumors, lymph nodes, and anomalies, collaborating with radiologists to ensure clinical accuracy. Implemented a tiered QA system, reducing labeling errors by 25% and improving model diagnostic precision to 98%. Tools: CVAT, 3D Slicer, DICOM viewers.

Developed guidelines for labeling tumors, lymph nodes, and anomalies, collaborating with radiologists to ensure clinical accuracy. Implemented a tiered QA system, reducing labeling errors by 25% and improving model diagnostic precision to 98%. Tools: CVAT, 3D Slicer, DICOM viewers.

2024 - 2024

Education

Work History