Thưởng T.

Key Skills

Software

Mindrift

OpenCV AI Kit (OAK)

Top Subject Matter

Legal Services & Contract Review

Regulatory Compliance & Risk Analysis

Legal Research & Document Analysis

Top Data Types

Computer Code Programming

Text

Document

Top Task Types

Fine-tuning

Polygon

Segmentation

Classification

Polyline

Cuboid

Object Detection

Text Generation

Question Answering

RLHF

Freelancer Overview

I have a strong background in software engineering, distributed systems, blockchain infrastructure, and technical evaluation workflows, with over 7 years of experience working across backend systems, smart contracts, cloud infrastructure, testing pipelines, and production-grade applications. While my experience is not traditional data labeling, much of my work involves analytical evaluation, debugging, system validation, adversarial testing, and reviewing complex technical implementations under real-world constraints. Recently, I have become increasingly interested in AI systems, AI agent evaluation, and reasoning-focused training workflows. My experience designing scalable systems, reviewing code quality, identifying edge cases, and validating correctness aligns well with AI training and evaluation projects. I am comfortable working with Python, Docker, CI/CD workflows, distributed architectures, testing frameworks, and technical documentation, and I enjoy collaborating in environments that require structured reasoning, attention to detail, and continuous learning.

Labeling Experience

AI Coding Agent Evaluation & Technical Benchmarking

DocumentFine-tuning

Contributed to AI training and evaluation workflows focused on technical reasoning, software engineering, and distributed systems analysis. Worked on reviewing and validating AI-generated technical outputs, including backend architectures, infrastructure configurations, blockchain-related systems, and programming tasks involving Python, Rust, TypeScript, and cloud-native environments. Responsibilities included analyzing generated code and technical solutions, identifying logical inconsistencies and edge cases, validating implementation correctness, improving prompt/task quality, and creating structured evaluation criteria for complex engineering scenarios. Projects involved technical reasoning tasks related to distributed systems, smart contracts, asynchronous workflows, infrastructure reliability, testing pipelines, and debugging environments. Additionally contributed to AI fine-tuning and evaluation processes by designing realistic engineering scenarios, reviewing multi-step reasoning outputs, and ensuring consistency, correctness, and robustness across generated solutions. Applied strong attention to technical accuracy, adversarial testing, and production-oriented engineering standards throughout the workflow.

2026 - Present

Education

Work History