Chen X.

Key Skills

Software

No software listed

Top Subject Matter

AI Model Logic Evaluation and Code Auditing

Code Review and Evaluation for AI Model Training

Textual Logic Evaluation and Financial Data Audit

Top Data Types

Computer Code Programming

Top Task Types

Classification

Entity (NER) Classification

Computer Programming/Coding

Freelancer Overview

AI Training Specialist & Logical Modeling Lead (Shanghai Yinhangzhan Project). Brings 10+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes Bachelor of Science, 内蒙古科技大学 (2017). AI-training focus includes data types such as Computer Code, Programming, and Text and labeling workflows including Evaluation and Rating.

Labeling Experience

AI Training Specialist & Logical Modeling Lead (Shanghai Yinhangzhan Project)

I led the logical modeling and rules evaluation for an AI training project, focusing on breaking down complex logic using Drools rules engine. My role involved verifying code correctness and auditing mathematical formulas, ensuring the AI-generated code adhered to production and safety standards. I provided structured feedback on edge cases, logic inconsistencies, and formula accuracy for model refinement. • Specialized in in-depth mathematical and logical verification for AI-generated Java code • Designed bonus calculation schemes based on nonlinear inference and conditional logic • Applied rigorous low-code templates and expression calculations for model validation • Ensured compliance with safety and production requirements in code outputs

2022 - Present

AI Code Review Expert (Unicom International Operation Platform)

As the lead architect, I audited AI-generated code modules and ensured high-quality standards through extensive code review and evaluation. I identified and fixed concurrency logic defects and stabilized system behaviors pertinent to model training. My work involved defining code generation and review standards to guide AI learning and validation processes. • Conducted in-depth code evaluation for AI-generated and human-written Java modules • Devised code generation templates to enforce standardization • Detected and resolved critical logic flaws for model reliability • Established protocols for quality and safety compliance in code review

2019 - 2022

AI Data Audit and Real-Time Inference Lead (Unicom International Audit Project)

Text

I designed and validated a real-time data audit and inference pipeline to enhance AI’s logic comprehension in the financial domain. My responsibilities included verifying dataset logical consistency, building audit feedback loops, and improving model response quality. The experience closely aligns with AI training tasks for detecting and correcting textual logic errors. • Engineered models for real-time detection of textual inconsistencies • Audited labeled financial datasets for precise error identification • Developed structured logic checks to support AI reasoning • Supplied actionable feedback to enhance model response accuracy

2017 - 2019

Education

Work History