David Z.

Key Skills

Software

Don't disclose

Other

Top Subject Matter

Financial document processing and enterprise SaaS pipelines

Image-based compliance detection and automated field validation

Legal Services & Contract Review

Top Data Types

Document

Image

Text

Top Task Types

Data Collection

Classification

Freelancer Overview

Fullstack SaaS Architecture & Enterprise Platform Delivery — Senior Full-Stack Engineer (Symphony). Brings 8+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Don't disclose and Other. Education includes Post Graduate Program, UT Austin, McCombs and AWS Solutions Architect – Associate, Amazon Web Services (AWS). AI-training focus includes data types such as Document and Image and labeling workflows including Data Collection and Classification.

Labeling Experience

Fullstack SaaS Architecture & Enterprise Platform Delivery — Senior Full-Stack Engineer (Symphony)

Don't discloseDocumentData Collection

Delivered a financial document processing workflow that required preparing and processing document inputs at scale for downstream AI/document understanding tasks. Built a pipeline to ingest and process 100,000+ records across multiple document formats (PDF, DOCX, CSV) and integrate them into real-time processing workflows. Focused on secure, production-grade handling of document data for consistent interpretation results in an enterprise setting. • Integrated Python/FastAPI services with Node.js for processing and orchestration. • Implemented real-time streaming interfaces to support timely document handling. • Applied OAuth2-based authentication and data encryption to protect document inputs. • Established automated CI/CD quality gates to ensure reliable document processing releases.

2022 - 2026

Key Project — Helmet Compliance Detection (Python, TensorFlow, VGG-16, Flask)

OtherImageClassification

Developed an automated compliance detection approach using CNN-based image classification for field validation use cases. Trained and deployed a Flask REST API to classify inputs (images) and return compliance-relevant predictions with high accuracy. Designed the solution to support automated evaluation rather than manual review workflows. • Used Python with TensorFlow and VGG-16 for model training. • Deployed as a production Flask REST API for automated validation. • Targeted compliance detection/classification as the primary supervised learning objective. • Packaged the workflow to integrate into larger applications and pipelines.

2019 - 2021

Education

Work History