AI Engineer × Growth Operator
9 years engineering. 2 years embedding AI into consumer products that ship, sell & scale.
I build the system, run the GTM, and let the agents work the night shift.
I'm Omar — a senior software engineer from Egypt who spent the last two years embedding AI into consumer-facing products: storefronts, exam prep, retail ops, B2B sourcing. The kind of stuff real customers click, complain about, and pay for.
Before that, nine years of full-stack engineering, e-commerce growth, paid acquisition, and digital transformation across MENA. I shipped React apps, scaled DTC stores to 2.5–3.2× ROAS, and ran GTM for 15+ brands before "prompt engineering" was a job title.
I sit at the intersection most engineers avoid: code, marketing, and operations. I architect the system, write the production AI pipeline, design the funnel, and let autonomous agents run the night shift. The result is products that don't just work — they convert.
From computer vision prototypes to multi-agent AI fleets — every mission built on the last.
Six autonomous products. Designed to run without me — from AI retail to open-source agent memory.
Full case studies with architecture diagrams and real numbers. Technical essays with per-change contribution breakdowns. An interactive sandbox of my open-source agent memory project. This is the section I'd send to someone who asked "but can he actually ship."
Six specific moves — task routing, semantic caching, prompt compression, structured output, batching, waste gatekeeping — with per-change contribution numbers.
Read the essay → Essay · 9 minA production LLM extraction pipeline at Bridge Sourcing went from 82% to 96% over three months. The eight changes, and the two that made it worse.
Read the essay → Case studyPaying customer in Egypt. ~4% MAPE forecasting, 218ms POS sync, what-I-got-wrong section, SVG architecture diagram.
Read the case study → Case study7,725 questions, Telegram Mini App, the explanation pipeline that turns testing into teaching.
Read the case study → Case studyEU buyers ↔ Egyptian suppliers. Three agents, one rubric that does most of the work.
Read the case study → Interactive LabStore memories, watch auto-classification, run semantic queries, see contradictions flagged. Pure client-side, no API calls.
Open the sandbox →Six autonomous products. Multi-agent pipelines running 24/7. Cost-optimized LLM routing. This is what operating at scale looks like from the cockpit.
These three tools exist because I use them in my own work. They're not a funnel — no email capture, no tracking, no "send to Omar" buttons in the outputs. If you find them useful, use them. If not, keep scrolling to the work that matters.
Building AI systems, exploring new missions, or just want to talk autonomous agents — I'm reachable across all frequencies.