Yousef Wahba
Senior Full-Stack AI Engineer
I architect, build, secure, and ship AI products
from the first system decision to production.
01 / Introduction
Across product, models, infrastructure, and security, I turn working prototypes into dependable software built to ship, scale, and earn trust.
Architecture → Product
Prototype → Production
Six products where intelligence met engineering — each one shipped, operated, and proven in production.
One trip. Multiple specialists. One intelligent system.
A multi-agent travel system built around LangGraph for solving complex travel workflows through specialized agents covering destinations, tours, pricing, booking and planning.
A retrieval-augmented system that grounds every answer in real knowledge — from document ingestion to embedding, retrieval, contextual assembly and final grounded response.
A marketplace where AI is woven into the core experience — search, discovery, recommendations and merchandising — backed by a full marketplace architecture and AI-native full-stack engineering.
Multiple organizations living inside one larger architecture while retaining fully separate data, permissions, and identity boundaries. Tenant isolation by design, not by accident.
An AI website and application generation platform. A prompt becomes a real, deployable full-stack app — generated, orchestrated and sandboxed end to end.
Generative AI capability turned into a complete SaaS experience — with billing, workspaces, accounts, and the operational surface expected of any serious product.
Transition
The discipline
I work with open-source models, OpenAI and Anthropic, autonomous agents, retrieval-augmented generation, the Model Context Protocol, and intelligent workflows — turning capability into systems that reason, retrieve, and act.
A powerful model isn't a product. The experience, the frontend, the APIs, the intelligence layer, the data and the infrastructure — all of it has to hold together as one system.
A powerful model isn't a product.
I don't stop at the model.
I build the product around it.
DevOps and MLOps as a continuous practice — code, build, containerize, deploy, observe, scale. Operating intelligent systems in production means owning every transition from commit to live traffic.
Build it. Ship it. Operate it. Improve it.
It starts with architecture. Authentication, authorization, API surface, data, infrastructure, application — each layer treated as a boundary that has to be deliberately designed.
It starts with architecture.
Owning technical decisions, architecture and delivery across every layer of a product. Translating ambition into systems that can be built, shipped and operated by real teams.
From idea to architecture.
From architecture to production.
Philosophy
Foundation
The equation
Dive into the problem, the users, the constraints and the ambition. What does done look like?
Design the system before writing a line of product code. Intelligence, data, infra, security — one picture.
Implement with rigor across the stack. Frontend, backend, AI layer, data — built to be operated.
Threat-model every boundary. Authentication, authorization, data, API, infrastructure, application.
Continuous build, containerize, deploy, observe, scale. From commit to live traffic without ceremony.
Own the technical direction. Translate ambition into systems real teams can build, ship and operate.
Five years building software across mobile, full-stack, SaaS, e-commerce, DevOps and security — each discipline sharpening the next.
Today that entire foundation comes together in a single practice: architecting, building, securing, deploying and leading serious AI products end-to-end. Not integration work. Production AI engineering.
Contact
I'm interested in working with teams and founders building serious technology — particularly products where AI is fundamental to the experience.