Cadyn G.

Key Skills

Software

No software listed

Top Subject Matter

AI evaluation and model assessment infrastructure

AI evaluation tooling and dashboards

Machine learning and NLP application development

Top Data Types

Text

Top Task Types

Fine-tuning

Freelancer Overview

Senior Full-Stack / AI Engineer. Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes Master of Science, Illinois University (2026) and Bachelor of Science, Illinois University (2023). AI-training focus includes data types such as Computer Code and Programming and labeling workflows including Evaluation, Rating, and Fine-tuning.

Labeling Experience

Handshake AI — Senior Full-Stack / AI Engineer (2025–Present)

Built and deployed distributed AI evaluation workflows using high-throughput processing systems. Developed production APIs and backend services to support AI evaluation pipelines and model assessment. Improved scalability and maintainability of backend components to ensure reliable evaluation at scale. • Distributed AI evaluation and high-throughput processing • Python/FastAPI-based API development for evaluation services • Backend revamp for scalability, reliability, and maintainability • Integration with PostgreSQL and Redis for evaluation pipeline state and performance

2025 - Present

OutlierAI — Full-Stack Software Engineer (2024–Present)

Developed scalable evaluation tools and dashboards for assessing AI outputs. Built backend services and asynchronous processing pipelines to support evaluation workflows. Implemented CI/CD workflows to streamline deployment of evaluation-related services. • Evaluation tools and dashboard development • Asynchronous processing pipelines for evaluation tasks • Backend service development for scalable evaluation • CI/CD automation for evaluation tooling releases

2024 - Present

IBM — Machine Learning Engineer (2022–2023)

Fine-tuning

Built machine learning and NLP applications along with scalable APIs that likely supported training or model-serving workflows. Implemented production-ready components to enable NLP functionality. Supported ML application infrastructure development through engineering of backend services. • Machine learning and NLP application development • Scalable APIs for ML/NLP services • Production backend engineering for AI capabilities • Engineering support for ML workflow enablement

2022 - 2023

Education

Work History