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Carter AI

Automation Engineer at Carter AI building high-scale scraping and ETL systems for AI shopping intelligence, with a 99.9% retrieval success rate.

workflow-automationweb-scrapingpythonpuppeteerplaywrightetlseleniumbeautifulsoupmultiprocessingai-shopping

✓ 99.9% data retrieval success rate

GitHub Live

Carter AI - Automation Engineer (Internship)

Timeline: Nov 2025 - Present
Location: Pune District, Maharashtra, India (Hybrid)

As a foundational engineering team member at Carter AI, I architected and maintained high-scale web scraping pipelines that powered AI shopping algorithms.

Core Contributions

  • Designed and deployed headless browser workflows with Puppeteer and Playwright for real-time pricing and inventory extraction from complex e-commerce sites.
  • Built resilient data acquisition systems for JavaScript-heavy environments and unstable page structures.
  • Implemented robust ETL pipelines to transform unstructured HTML into normalized JSON datasets for downstream AI model consumption.
  • Maintained operational reliability at scale with strong automation and monitoring practices.

Impact

  • Sustained 99.9% data retrieval success rate across dynamic scraping workloads.
  • Enabled AI systems to process consistent market data with reduced latency and improved freshness.

Related Project: Dynamic Web Scraper (Oct 2025)

A Python-based scraper designed to dynamically navigate e-commerce websites, collect product listings from category pages, and extract detailed product information at the item level.

Live Product