AI & Backend Engineer

Designed and built an AI advertising platform from scratch, sustaining 20–30k ads/day with TypeScript, Python/FastAPI services, Google Cloud, Google Gemini, and Meta Ads API.

Took the system from an n8n prototype to a production event-driven backend in about 6 months, collaborating through a typed integration contract and automated QA gates.

Delivered a finance document intelligence RAG pipeline and optimized Stable Diffusion inference for high-traffic production systems. Also built automation workflows with n8n, Make.com, and Zapier for lead enrichment, routing, and notifications.

Redesigned PostgreSQL schema and query paths across Go, Python, and TypeScript backend services, reducing p95 read latency from about 410ms to 290ms.

Added Redis LRU caching for session and usage data, lowering database round-trips per request by roughly one third.

Improved Docker build caching and image layering, cutting CI image build time from about 9 minutes to 4.5 minutes.

Built Go, Python, and TypeScript microservices for a real-time shipment tracking system serving about 12K monthly active users.

Implemented configurable WebSocket APIs and modeled tracking events and shipment history in MongoDB, while keeping missed or duplicate notifications below 1%.

Migrated builds to Jenkins pipelines and reduced deploy preparation time from around 40 minutes to under 10 minutes.

Built scalable Go and Python microservices on Kubernetes and Docker with PostgreSQL and MongoDB, supporting growth beyond 100K active users.

Introduced Redis caching on hot endpoints, roughly halving p95 response time under peak traffic.

Secured service-to-service traffic with mTLS and maintained consistent sprint delivery.

Bachelor's degree in Computer Science.