Bashar Ayyash

AI Engineer & Full-Stack Tech Lead Helping Businesses Grow with AI

I'm a Tech Lead and AI Engineer based in Amman, Jordan, specializing in Laravel, Next.js, React Native, and AI agents (RAG systems) for MENA businesses. Bridging the gap between 20 years of expertise and modern AI Agents.

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hello@yabasha.dev

For consulting, architecture reviews, or project inquiries.

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Base

Amman, Jordan 🇯🇴

Available for Consulting

The Arsenal

LaravelNext.js 16BunReact NativeTailwind v4OpenAI APIDockerPostgreSQLTypeScript
20+Years Experience
50+Projects Shipped
MENARegion Focus
AI + CodeFull-Stack Delivery

In one minute

Yabasha (Bashar Ayyash) is an AI Engineer and full‑stack tech lead based in Amman, Jordan. He helps MENA founders, CTOs, and product teams design and ship dependable software, then layer in AI where it drives measurable outcomes. His work spans system architecture, hands‑on implementation, and production hardening—security, performance, and observability—so products stay stable under real traffic. Typical engagements start with a short discovery and a vertical slice, then iterate weekly with documentation and handover. If you need a senior engineer who can own delivery end‑to‑end, he can help.

Key offerings

  • RAG assistants and tool‑using agents grounded in your docs, tickets, and databases
  • Laravel + Next.js platforms with auth, payments, and dashboards
  • React Native mobile app development for offline sync, performance tuning, and reliable app store releases
  • Workflow automation and integrations (queues, ETL, notifications)

Differentiators

  • Clear scope, fast feedback loops, and senior end‑to‑end ownership
  • Practical AI: evals, monitoring, and guardrails (not demos)

Who I Help

CTOs, product leaders, and founders in MENA who need a senior engineer to design and ship AI-powered products end-to-end.

Fintech & Mobile

React Native apps with payment integrations, secure flows, and real-device performance tuning.

AI & Data Platforms

RAG assistants, agent orchestration, offline-first sync, and data pipelines at scale.

SaaS & Operations

Laravel + Next.js platforms with multi-DB backends, queues, dashboards, and audit trails.

Dev Tools & Infra

Internal utilities, automation scripts, open-source tooling, and Laravel AI SDK work.

Web & Mobile Platforms

Robust, scalable platforms using Laravel for backend and Next.js for high-performance frontends.

Mobile App Development

Native-feel iOS and Android applications built with React Native and Expo. View mobile app service details.

AI Chatbots & Assistants

Production RAG assistants and tool-using agents that answer from your docs, tickets, and databases — with citations, access control, and evaluation. Read the RAG guide.

Experience & Proof

Experience across enterprise delivery and fast-moving product teams in the MENA region. References and deeper case studies are available on request.

Enterprise (NDA)Banking (NDA)E‑commerceSaaSSupply chainHR / procurement

How We Work

Clear scope, fast feedback loops, and production-quality delivery—without ceremony.

  1. 1. Scope & Success Metrics (2–5 days) — clarify the journey, constraints, risks, and define KPIs/SLAs/acceptance criteria.
  2. 2. Architecture & Plan (3–7 days) — choose the approach, map milestones, and lock the first deliverable.
  3. 3. Build the Vertical Slice (1–2 weeks) — deliver an end-to-end slice (UI → API → DB → observability) to validate direction early.
  4. 4. Iterate to Full Feature Set (2–6+ weeks) — ship weekly increments: features, edge cases, performance work, and stakeholder feedback loops.
  5. 5. Hardening, Launch & Ongoing Improvement (1–2 weeks + ongoing) — testing, security review, monitoring, rollout plan, and handover docs.

Production-Grade Standards

I optimize for reliability and maintainability—so the system keeps working when it's under load, audited, or handed to a team.

  • Testing by default: unit + integration + e2e where it pays off; realistic fixtures; regression coverage for critical flows.
  • CI/CD discipline: automated lint/type-check/tests, preview builds, safe migrations, and repeatable deployments.
  • Observability built-in: structured logs, metrics, traces, alerts, and dashboards tied to business KPIs (not just CPU).
  • Security-first engineering: least privilege access, secrets management, input validation, audit logs, and threat-aware reviews.
  • Performance budgets: latency targets, caching strategy, background jobs/queues, and profiling—not "optimize later."
  • React Native quality bar: crash-free sessions, offline handling, slow-device testing, startup performance, and app-store-ready release processes.
  • Data reliability: idempotent sync, conflict handling, backfills, and safe reprocessing for large-scale datasets.
  • AI safety & evals: prompt/version control, eval datasets, hallucination/grounding checks, tool-use guardrails, and human approval gates where needed.

FAQ

Use RAG when your answer must be grounded in changing or proprietary knowledge (docs, tickets, policies) and you need citations. Consider fine-tuning when you want consistent style/format, domain-specific behavior, or tool-selection patterns—and your knowledge is stable. Many real systems use both: RAG for facts, light tuning for behavior.

Typically by adding an AI service layer (queues, retries, rate limits), a small adapter for providers, and an audited tool/API boundary. We start with 1–2 high-value workflows, add observability and evals, then expand. The goal is incremental adoption: minimal disruption, measurable outcomes.