ahrasel monogram
01 / Engineering

Capabilities, architecture, and evidence.

How I contribute to an engineering team or product — a practical map of backend development, data, infrastructure, delivery and architecture.

Capabilities

What I do

01

Backend Engineering

Production-ready backend systems using Laravel/PHP, Node.js and TypeScript — APIs, business logic, authentication, integrations and database architecture.

Laravel PHP Node.js TypeScript Microservices GraphQL
02

Data & Infrastructure

Relational and NoSQL databases, caching layers and the infrastructure that keeps them available.

MySQL MongoDB Redis Firebase Docker Kubernetes
03

Delivery & DevOps

Containerizing applications, building deployment workflows and improving delivery reliability.

Docker Kubernetes
04

System Architecture

Turning requirements into maintainable architectures, modular systems and clear service boundaries.

Database Design Microservices GraphQL
05

Frontend & Mobile

Vue.js, Nuxt, Next.js, TypeScript and Flutter — building the interfaces that consume the systems I ship.

TypeScript JavaScript Next.js Vue.js Nuxt.js Flutter
AI & Modern Engineering

AI in my practice

01

AI-Assisted Development

VERIFIED

I use AI coding tools daily — AI agents, Copilot, ChatGPT and Claude — to develop, debug, review, document and test software. AI is part of my normal engineering workflow.

02

AI Application Engineering

VERIFIED · TRANSFERABLE

I have shipped a production AI/LLM integration and I work across the layer it lives on: backend APIs, authentication, Redis caching, queues and async jobs.

03

Backend & AI Infrastructure

TRANSFERABLE

Docker, Kubernetes, cloud servers and DevOps are exactly what it takes to package, deploy and operate AI-powered services reliably.

04

Currently Exploring

EXPLORING

LLM application architecture · RAG · AI agents · vector search · MCP servers · AI observability and evaluation.

For engineering leadership

Where I fit in an AI-driven engineering team

My value isn't simply knowing how to call an LLM API. It's the production backend, data, infrastructure and deployment that make AI features real — plus the ability to integrate the AI layer itself.

Each layer below is a capability I bring from shipping production systems. The AI application layer is where I'm actively building.

01 AI Model / AI API
02 AI Application Layer
03 Backend Services
04 MySQL / Redis
05 Queues / Workers
06 Docker / Kubernetes
07 CI/CD
08 Cloud Infrastructure
AI Learning Direction

Where I'm going

01 LLM Application Architecture
02 RAG Systems
03 AI Agents
04 Vector Search
05 AI Backend Patterns
06 AI Infrastructure
07 LLM Observability
08 AI Evaluation
Architecture

How production systems are put together

The pattern I build toward: an application layer over clear service boundaries, backed by durable data and wrapped in containerized, observable delivery.

01 Application
02 API Layer
03 Services
04 MySQL / Redis
05 Docker
06 Kubernetes
07 Infrastructure
Case Studies

Detailed engineering work

All projects

SAAS

Online Learning Management System

Online learning management system developed with PHP and Laravel.

Laravel PHP

BUSINESS APPLICATION

Digital Pharmacy

Pharmacy management system built with Laravel and Flutter.

Laravel Flutter

E-COMMERCE

Unexcart E-commerce Platform

E-commerce platform built with Laravel, Nuxt.js and Vue.js.

Laravel Vue.js Nuxt.js

Categories