Jiang Chang

About Jiang Chang

Enthusiastic Software Engineer and Data Scientist passionate about applying AI to solve real-world problems. I design and deploy intelligent systems that accelerate R&D, optimize production, and unlock operational efficiency — from Retrieval-Augmented Generation (RAG) pipelines and computer vision monitoring to AI-driven resource allocation and scheduling.With experience spanning AI, full-stack engineering, and materials science, I’ve delivered solutions for industries from finance to energy storage. My work blends cutting-edge machine learning with scalable software architecture to turn data into action.Dual Master’s degrees from Carnegie Mellon in Information Systems Management and Materials Science & Engineering. I thrive at the intersection of AI innovation and practical business impact.

Experience

1. Principal Software Engineer (Ai & Data Platform)

   Stratus Materials Inc.

   Jun 2023 — Present · Pittsburgh, PA, US

   AI Systems- Architected and productionized an end-to-end RAG platform using LangChain, OpenAI, and ChromaDB to accelerate R&D knowledge retrieval and reduce manual document analysis- Designed prompt-engineering and retrieval strategies to improve structured data extraction accuracy from multimodal data- Led development of a computer vision pipeline that extracts telemetry directly from PLC interfaces for real-time operational monitoring- Initiated an AI agent framework for intelligent production resource allocation and scheduling.Data Platform- Designed and implemented a company-wide data platform spanning R&D, quality, and production operations- Partnered with stakeholders to translate manufacturing KPIs into scalable database schemas and analytics models- Built a modular data architecture (React + Flask + PostgreSQL) enabling operators to capture process data across production stages- Developed scalable pipelines integrating analytical equipment, facility telemetry, and testing workflows- Deployed and customized Apache Superset to deliver advanced quality analytics and operational dashboards.Infrastructure & Performance- Architected a high-availability PostgreSQL system with replication, partitioning, and automated failover to support mission-critical production workloads- Improved query performance through indexing and schema optimization, enabling scalable analytics across growing datasets- Designed automated data quality validation and alerting systems to ensure reliability of operational metrics.

Education

• Carnegie Mellon University

  Master of Materials Science and Engineering

  2013 — 2014

• Carnegie Mellon University

  Master of Information Systems Management

  2019 — 2020