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Mohamed-Sherif-Ali/README.md

Mohamed Sherif Ali

AI Agents & Automation Engineer building practical LLM applications, automation systems, backend APIs, and deployment-oriented agent workflows.

I focus on connecting AI models with real business systems: APIs, webhooks, bots, WhatsApp flows, Google Sheets integrations, and safe deployment workflows.

Current Focus

  • AI-agent workflow design and orchestration
  • Python and FastAPI backend development
  • OpenAI API integrations and conversational systems
  • APIs, webhooks, bots, and business workflow automation
  • Deployment readiness, safety checks, and structured operational workflows

Featured Work

Jarvis - Deployment Orchestration Agent

Private implementation, public case study.

An AI-agent system I built end-to-end to inspect projects, evaluate deployment readiness, check risks, recommend compatible hosting paths, and produce deployment/rollback guidance.

Areas: AI agents, deployment automation, safety checks, structured workflows, schemas, policies, playbooks

Doubly AI Chatbot Backend

Private implementation, public case study.

A bilingual chatbot backend for customer communication and lead capture, integrating OpenAI API, WhatsApp workflows, Google Sheets persistence, and deployment-oriented backend configuration.

Areas: Python, FastAPI, OpenAI API, WhatsApp workflows, Google Sheets, webhooks

Botter

A skill-based builder that turns Claude Code into a focused assistant for creating Discord and Telegram bots from plain-language instructions.

Areas: AI-assisted development, bot generation, automation, TypeScript, Discord, Telegram

Hooked - Conversational Webhook Builder

A conversational system for creating and configuring Discord automations and webhooks using natural-language instructions.

Areas: Claude Agent SDK, TypeScript, Discord, webhooks, automation, scheduling

DriveSentinel

A computer-vision and embedded-systems prototype for driver monitoring, lane detection, parking assistance, and automated safety responses.

Areas: Python, OpenCV, MediaPipe, Raspberry Pi, ESP32

Cats vs Dogs Classifier

A transfer-learning image-classification project using VGG16 and ResNet50, with validation accuracy around 96% in the strongest experiment.

Areas: TensorFlow, Keras, transfer learning, model evaluation, computer vision

Other Projects

  • SecurePass - local desktop password manager with authenticated encryption.
  • FireAlert - ESP32 fire-detection prototype with automated WhatsApp alerts.

Technologies

AI and automation: OpenAI API, LLM applications, AI-agent workflows, RAG concepts, prompt and instruction design
Backend: Python, FastAPI, REST APIs, webhooks, JSON, SQLite, PostgreSQL fundamentals
Integrations: WhatsApp workflows, Google Sheets, Discord webhooks, third-party APIs, Postman
Infrastructure: Git, GitHub, Docker fundamentals, Railway, Netlify, Linux/VPS basics
Web and systems: HTML, CSS, JavaScript fundamentals, OpenCV, TensorFlow, Raspberry Pi, ESP32

Contact

Pinned Loading

  1. DriveSentinel DriveSentinel Public

    Driver-safety prototype combining drowsiness detection, lane detection, parking assistance, and Raspberry Pi-ESP32 control.

    C++

  2. cats-vs-dogs-classifier cats-vs-dogs-classifier Public

    Cats vs dogs image classifier using TensorFlow/Keras transfer learning with VGG16 and ResNet50, reaching about 96% validation accuracy.

    Jupyter Notebook

  3. SecurePass SecurePass Public

    Local desktop password manager using AES-256-GCM encryption, PBKDF2 key derivation, SQLite, and PyQt5.

    Python

  4. FireAlert FireAlert Public

    ESP32 fire-monitoring prototype using flame and temperature sensors, LCD status, audible alarms, and WhatsApp alerts.

    C++