GusDev
AI Automation Engineer architecting production-grade systems around a 20-year-old POS, syncing bidirectionally with PostgreSQL and delivering staff and customer-facing AI experiences (WhatsApp agent).
About
AI Automation Engineer architecting production-grade systems around a 20-year-old POS, syncing bidirectionally with PostgreSQL and delivering staff and customer-facing AI experiences (WhatsApp agent). Builds and operates unattended 24/7 automation and AI agents with n8n, RAG pipelines, vector search, and evaluation/benchmarking, along with full-stack products (mobile + React Native) and hybrid integrations between legacy POS and cloud services.
- 3 years' experience
- Joined August 2026
Skills· 16
Languages
- Spanish; CastilianFluent
- EnglishAdvanced
Industry experience
- Retail
- Software & Internet
- Computers & Electronics
- Financial Services
- Professional Services
Work experience· 9 roles
Built daily sales analytics with full invoice history and live inventory workflows for staff. Added quote building with PDF export and enabled purchases to queue into the POS through a write-back bridge mechanism. Shipped Android and PWA from an Expo/React Native/TypeScript codebase. Designed a three-tier role model with approval gates and implemented anomaly detection using Gemini, supporting inventory/sales monitoring workflows while ensuring requests are queued rather than directly rewritten to the POS.
Built daily sales analytics including full invoice history and live inventory for staff. Added quotation building with PDF export and queued purchases to be written back into the POS via bridge listeners. Implemented a three-tier role model with approval gates and Gemini-based anomaly detection. Shipped an Android app and PWA using an Expo/React Native/TypeScript codebase. Designed it to avoid direct POS writes by enqueuing requests and replaying through bridge listeners.
Built and operated an autonomous WhatsApp AI agent on n8n and Docker (WAHA, OpenAI, Groq Whisper). Designed a 5-layer retrieval pipeline over ~7,650 SKUs with common query resolution around ~900ms. Served 191 customers since June with high containment (only 37 conversations required a human). Built a 320-case evaluation benchmark for agent failures, improving exact recall from 72.2% to 76.9%. Increased vector signal-to-noise by embedding enriched text rather than raw catalog data. Delivered hybrid on-cloud bidirectional bridging between legacy POS (Delphi/BISAM POS) and Supabase, including BISAM data parsing and robust write-back through the POS UI.
Education
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