AI & Data Engineering
Production retrieval-augmented systems and private model serving.
- RAG pipelines
- Model Context Protocol (MCP)
- Ollama — DeepSeek / Qwen
- pgvector vector search
I design and build complex cloud platforms — multi-tenant SaaS, custom RAG pipelines, and the high-availability infrastructure that runs them. From system architecture and AI integration to DevOps and security, I take products from concept to resilient production.
01 — Core capabilities
Three disciplines I combine to ship resilient products: applied AI, enterprise backend architecture, and the infrastructure that keeps it all online.
Production retrieval-augmented systems and private model serving.
Enterprise platforms designed to stay stable under real load.
Self-managed cloud infrastructure with automated delivery.
02 — Curated architecture
Several of these are internal or NDA-protected enterprise systems. Each card opens the architecture and the engineering decisions behind it — challenge, solution, and stack.
Multi-tenant cloud ERP with an AI agent layer that automates accounting workflows and module generation.
Retrieval-augmented generation pipelines for SaaS platforms, running open models privately to cut API cost.
Bilingual marketing site for Masar ERP (masarerp.com) — ZATCA-certified cloud accounting, multi-branch inventory, POS, and sector pages built for Saudi discovery and conversion.
Company digital platform with a performance-first build and an SEO foundation tuned for discovery.
Self-managed cloud servers with fully automated deployment and hardened security for 99.9% uptime.
Logistics platform modeling the full trip lifecycle with live tracking and driver workflows.
03 — Let's connect
Whether it's an AI-driven platform, a multi-tenant SaaS, or hardening your cloud infrastructure — tell me the problem and the constraints. I reply when I can add clear engineering value.