v2.0.0 · Currently available for projects

Software Engineer
AI & Automation Specialist

I build

Engineering high-performance systems at the intersection of full-stack development, artificial intelligence, and intelligent automation. I transform complex requirements into scalable, production-ready solutions.

5+
Years Experience
30+
Projects Delivered
15+
Happy Clients
What I Do

Engineering Excellence

Full-spectrum software engineering from architecture to deployment

CRM & Enterprise Systems

End-to-end development of custom CRM platforms, ERP modules, and business management dashboards with real-time analytics and role-based access control.

React Node.js PostgreSQL Redis Docker

AI & Automation

Intelligent automation pipelines, NLP-powered chatbots, document processing, and ML model deployment. Turning data into decisions with minimal latency.

Python TensorFlow LangChain OpenAI RabbitMQ

High-Performance Web

Blazing-fast, SEO-optimized websites and web applications with sub-second load times, perfect Lighthouse scores, and buttery-smooth UX.

Next.js TypeScript Tailwind Vercel Cloudflare

Security & DevSecOps

Zero-trust architecture, penetration testing, CI/CD pipeline hardening, and real-time threat monitoring. Security baked in at every layer.

AWS Kubernetes Terraform GitHub Actions WAF

Data Engineering

Scalable data pipelines, ETL processes, real-time streaming analytics, and data warehouse architecture for actionable business intelligence.

Apache Kafka Spark Snowflake dbt Airflow

Full-Stack Consulting

Technical strategy, architecture reviews, code audits, and team mentoring. From startup MVPs to enterprise-scale distributed systems.

System Design Microservices GraphQL gRPC Event Sourcing
Featured Work

Selected Projects

Real systems engineered for real impact

CRM WhatsApp AI Automation
Poseidon CRM

WhatsApp-integrated smart CRM with AI-powered booking automation for travel & tourism enterprises.

Read Case Study
AI ML Analytics Python
PredictiMind

AI-driven predictive analytics platform for customer behavior forecasting and sales intelligence.

Read Case Study
Chatbot NLP Healthcare LangChain
MediBot AI

Intelligent healthcare chatbot handling patient triage, appointment scheduling, and medical inquiries 24/7.

Read Case Study
Automation IoT Logistics Cloud
StockFlow

End-to-end warehouse & logistics automation with real-time inventory tracking and smart reordering.

Read Case Study
PWA Next.js E-Commerce AI
ShopSmart PWA

Ultra-fast progressive web app with AI-powered recommendations, boosting conversions by 35%.

Read Case Study
Dashboard Maritime Real-time IoT
MarineOps Dashboard

Real-time operations control center for maritime tourism companies with fleet tracking & alerting.

Read Case Study

Poseidon CRM — Smart Travel Management Suite

ReactNode.jsTwilioMongoDBAI
How It Works
1
WhatsApp Integration

Customer sends a booking inquiry via WhatsApp. The system captures the message through Twilio API and normalises the unstructured text into structured data using an NLP classifier.

2
AI Intent Parsing

A fine-tuned DistilBERT model extracts intent, dates, destinations, party size, and special requests from the conversation with 97% accuracy, routing each inquiry to the correct pipeline.

3
Automated Booking Pipeline

The system checks real-time availability across connected APIs (hotels, flights, cruises), presents options back to the client, and processes confirmed bookings end-to-end without human touch.

4
CRM Sync & Analytics

Every interaction is logged in the CRM with full customer history. Agents get a unified dashboard with real-time KPIs, conversion funnels, and automated follow-up reminders.

Tech Stack
Frontend
React 18TypeScriptTailwindRecharts
Backend
Node.jsExpressSocket.IORedis
AI & NLP
DistilBERTspaCyTensorFlowTwilio AI
Cloud & DB
AWS EC2MongoDB AtlasDockerNginx
Client Reviews
★★★★★
The WhatsApp integration completely transformed our booking process. What used to take 15 minutes of back-and-forth now happens in under 60 seconds. Our conversion rate jumped 40% in the first quarter.
MA
Mohamed Al-Rashid
CEO, Arabian Horizons Travel
★★★★★
We'd been struggling with fragmented systems for years. Poseidon unified everything — WhatsApp, emails, phone calls, all in one dashboard with beautiful analytics. The AI agent suggestions saved us thousands in training costs.
LK
Leila Khoury
Operations Director, Nile Cruise Lines
★★★★☆
The system is outstanding in English and Arabic. However, since we serve a significant German-speaking customer base, we needed the dashboard in German from day one. The team added it within two sprints, but it would have saved us time if it shipped with multi-language support out of the box.
HS
Hans Schubert
IT Manager, Europa Reisen GmbH

PredictiMind — AI Sales & Customer Analytics Platform

PythonFastAPIscikit-learnReactPostgreSQL
How It Works
1
Multi-Source Data Ingestion

The platform connects to CRM, e-commerce, social media, and POS systems via REST APIs and webhooks. Data is ETL'd into a unified warehouse with automated schema mapping and deduplication.

2
Feature Engineering Pipeline

Over 200 behavioural features are computed — recency/frequency/monetary, session patterns, churn signals, seasonal trends — and fed into the ML engine for training and inference.

3
Ensemble Prediction Models

An ensemble of XGBoost, LightGBM, and a deep neural network generates daily predictions for churn risk, next-purchase probability, customer lifetime value, and optimal discount thresholds.

4
Interactive Dashboard & Alerts

Business users explore predictions through an interactive React dashboard with drill-down filters. Slack and email alerts notify teams when a high-value customer shows churn risk above 70%.

Tech Stack
Frontend
React 18D3.jsTypeScriptMaterial UI
Backend
PythonFastAPICeleryRabbitMQ
AI & ML
XGBoostLightGBMTensorFlowSHAP
Cloud & DB
AWS SageMakerPostgreSQLRedisDocker
Client Reviews
★★★★★
PredictiMind caught a 23% churn risk in our top-tier segment three weeks before they started leaving. We launched a targeted retention campaign and saved over $180K in quarterly revenue. The ROI was immediate and measurable.
SJ
Sarah Jenkins
VP of Customer Success, TechFlow Inc.
★★★★★
We went from gut-feel decisions to data-driven precision. The dashboard is intuitive enough that our non-technical managers use it daily. The SHAP explainability feature helped us win board-level trust in the ML recommendations.
RC
Rafael Costa
Head of Analytics, MercadoLink
★★★★☆
A powerful platform overall. Our only friction point was the initial data integration — we have legacy systems with non-standard APIs. The engineering team was very responsive and custom-built 3 connectors for us, but a broader library of pre-built connectors would have accelerated the onboarding significantly.
AT
Aisha Tandon
CTO, OmniRetail Group

MediBot AI — Intelligent Healthcare Assistant

LangChainOpenAIReactFirebaseNLP
How It Works
1
Patient Query Intake

A patient describes their symptoms via the web widget or WhatsApp. MediBot uses a fine-tuned LLM with medical context to parse the query, identify urgency, and extract key clinical entities.

2
Symptom Analysis & Triage

The AI cross-references symptoms against a structured medical knowledge base using RAG (Retrieval-Augmented Generation) to suggest possible conditions, severity level, and recommended action — home care, clinic visit, or emergency.

3
Appointment Scheduling

If a consultation is needed, MediBot checks the clinic's real-time calendar, offers available slots, confirms the booking, and sends a calendar invite with pre-visit instructions — all within the same conversation thread.

4
Post-Visit Follow-Up

After the appointment, MediBot sends automated medication reminders, follow-up questionnaires, and satisfaction surveys. Doctors receive a summary of the AI-patient interaction for their records.

Tech Stack
Frontend
ReactTypeScriptTailwindPWA
Backend
Node.jsExpressWebSocketFirebase
AI & NLP
LangChainGPT-4PineconeRAG Pipeline
Cloud & DB
FirebaseMongoDBDockerVercel
Client Reviews
★★★★★
MediBot handles 70% of our patient inquiries completely autonomously. Our reception team can now focus on complex cases instead of answering the same questions about hours and insurance. Patient satisfaction scores are up 28%.
DN
Dr. Nadia Ibrahim
Medical Director, Cairo Health Center
★★★★★
The triage accuracy is remarkable. In six months of deployment, MediBot correctly identified 3 urgent cases that patients themselves had downplayed. The RAG approach gives us confidence that responses are grounded in verified medical sources.
JM
Dr. James Mwangi
Chief of Staff, Nairobi Medical Clinic
★★★★☆
Excellent chatbot with solid medical grounding. The one area for improvement is voice integration — many of our elderly patients prefer speaking over typing. We discussed a voice-to-text layer with the team and it's on the roadmap. Once that ships, this will be a complete product.
RP
Dr. Robert Park
CEO, TeleMed Asia

StockFlow — Warehouse & Logistics Automation Engine

Next.jsPythonIoTGoogle SheetsDocker
How It Works
1
Real-Time Inventory Capture

Barcode scanners, RFID readers, and IoT weight sensors feed real-time stock data into the system. The platform reconciles physical inventory with digital records every 60 seconds, flagging discrepancies instantly.

2
Smart Reorder Engine

An ML model trained on 18 months of historical data predicts optimal reorder points considering seasonality, lead times, and supplier reliability. Purchase orders are auto-generated and sent to vendors via email and SMS.

3
Cloud Sheet Sync

All inventory data is bi-directionally synced with Google Sheets in near-real-time, giving non-technical managers a familiar interface to monitor stock levels, edit orders, and generate reports without touching the main system.

4
Logistics Optimization

Route planning algorithms optimise delivery schedules based on traffic, fuel costs, and order priority. Drivers receive optimised routes via a mobile PWA, and customers get live tracking links.

Tech Stack
Frontend
Next.jsTypeScriptPWAMapbox
Backend
PythonFastAPICeleryGraphQL
Automation
Google Sheets APIZapierMQTTNode-RED
Cloud & DB
AWSPostgreSQLRedisDocker Swarm
Client Reviews
★★★★★
StockFlow eliminated our manual inventory counts. What used to take a full weekend every month now happens in real-time. Our stock accuracy went from 82% to 99.7%, and we reduced carrying costs by 22% through smarter reordering.
TW
Thomas Wachowski
Supply Chain Director, EuroParts Distribution
★★★★★
The Google Sheets integration was a game-changer for us. Our warehouse managers love spreadsheets but needed real-time data. Now they get both — familiar interface with live backend sync. The logistics routing saved us 15% on fuel costs in the first month.
AL
Amara Lubega
Operations Lead, PanAfrica Logistics
★★★★☆
The system is excellent for desktop and tablet use. However, our field workers needed a dedicated mobile app with offline support, since warehouse connectivity is patchy. The team delivered an offline-capable PWA, but native mobile apps for iOS/Android would have been ideal from the start.
CY
Chen Yu
IT Director, Shanghai Logistics Hub

ShopSmart PWA — AI-Driven E-Commerce Platform

Next.jsTypeScriptStripeTensorFlow.jsRedis
How It Works
1
Instant-Load PWA Shell

The app loads as a progressive web app with sub-second initial paint. All static assets are pre-cached via service workers, and product pages are pre-rendered at build time using Next.js SSG for instant navigation.

2
Behavioural Tracking

User interactions — clicks, dwell time, scroll depth, cart events — are streamed to a real-time analytics pipeline. A lightweight TensorFlow.js model runs client-side to adapt the UI based on browsing patterns.

3
AI Recommendation Engine

A hybrid collaborative-filtering + content-based model generates personalised product recommendations. The model retrains every 6 hours on new interaction data, ensuring recommendations stay fresh and relevant.

4
Seamless Checkout

Stripe-powered checkout with Apple Pay, Google Pay, and saved-card support. One-click purchase for returning customers. The entire flow is optimised for conversion, averaging 2.3 seconds from cart to confirmation.

Tech Stack
Frontend
Next.js 14TypeScriptTailwindService Worker
Backend
Node.jsGraphQLStripeWebSocket
AI & ML
TensorFlow.jsCollaborative FilteringRedisPython ML
Cloud & DB
VercelPostgreSQLRedisCloudflare
Client Reviews
★★★★★
ShopSmart loads faster than any e-commerce site we've ever used. Our Lighthouse performance score went from 42 to 98. But the real win is the recommendation engine — it increased our average order value by 35% and repeat purchase rate by 28%.
MG
Maria Garcia
Founder, VogueStreet Fashion
★★★★★
We migrated from Shopify to ShopSmart and it was the best decision we made. The PWA capabilities meant we didn't need a separate mobile app. Our mobile conversion rate increased by 42% and the offline mode is a hit with customers in areas with poor connectivity.
AK
Ahmed Khalifa
CEO, CairoMart
★★★★☆
Exceptional platform with one gap — AR product preview. For our furniture business, customers frequently returned items because they didn't match the room scale. The team is currently building a WebXR-based AR viewer, and the early demos look promising. Once that's live, this will be unstoppable.
SL
Sofia Lindgren
E-Commerce Director, Nordic Home

MarineOps Dashboard — Maritime Operations Control Center

ReactNode.jsMapboxWebSocketMongoDB
How It Works
1
Fleet Telemetry Ingestion

GPS, engine telemetry, fuel sensors, and weather data from every vessel are streamed to a centralised event hub via satellite IoT. The system processes 10K+ events per second with sub-100ms latency.

2
Real-Time Geospatial Dashboard

A Mapbox-powered dashboard displays every vessel's real-time position, route history, ETA, and status. Operations managers can zoom from a fleet overview to individual ship telemetry with a single click.

3
Predictive Maintenance Engine

ML models analyse engine telemetry patterns to predict component failures 48-72 hours in advance. The system automatically schedules maintenance at the nearest port, reducing unplanned downtime by 60%.

4
Automated Alerting & Compliance

Geofencing triggers automatic alerts when vessels approach restricted zones or deviate from route. Compliance reports for port authorities and maritime regulators are auto-generated and submitted via API.

Tech Stack
Frontend
ReactTypeScriptMapbox GLD3.js
Backend
Node.jsExpressSocket.IOKafka
Data & IoT
MQTTKafka StreamsInfluxDBTelegraf
Cloud & DB
AWSMongoDBRedisKubernetes
Client Reviews
★★★★★
MarineOps gave us a live picture of our entire fleet for the first time. We used to rely on radio check-ins every 4 hours. Now we see engine health, fuel efficiency, and crew status in real-time. Fuel costs dropped 18% from route optimisation alone.
CM
Capt. Marco Silva
Fleet Operations Manager, MedStar Cruises
★★★★★
The predictive maintenance feature saved us from a catastrophic engine failure on our flagship vessel. The system flagged an anomaly at 6 AM, we inspected at 8 AM, and found a cracked piston ring. That single alert paid for the entire platform.
HA
Hassan Al-Ali
Technical Director, Gulf Marine Services
★★★★☆
The core dashboard is world-class. Our main request is deeper integration with third-party port management systems — we operate across 12 ports and each uses different software. The team has built 4 integrations so far with more on the way. A standardised API marketplace would accelerate this significantly.
EJ
Elena Johansson
Head of Maritime Operations, Scandic Ports
About Me

Engineer. Builder. Problem Solver.

A Computer Engineering graduate passionate about building systems that matter

1class A7med606:
2 def __init__(self):
3 self.role = "Software Engineer"
4 self.expertise = [
5 "Web Development",
6 "AI & Automation",
7 "System Architecture",
8 "DevSecOps"
9 ]
10 self.mission = "Build systems that scale"
11
12 def build(self, idea):
13 while idea.not_done:
14 idea.refine()
15 idea.prototype()
16 idea.ship()
17 return "Production-Ready Solution"

Hi, I'm A7med

A Computer Engineering graduate with a deep passion for software architecture, artificial intelligence, and automation. I don't just write code — I engineer systems that are robust, scalable, and maintainable.

My expertise spans the full stack: from crafting pixel-perfect UIs with React and TypeScript, to designing fault-tolerant backends with Node.js and Python, to deploying AI models that automate real business workflows.

I believe the best systems emerge at the intersection of clean architecture, intelligent automation, and relentless curiosity. Every project is an opportunity to push boundaries and deliver measurable impact.

B.Sc. Computer Engineering 5+ Years Experience 30+ Projects Open Source Contributor
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Let's Build Something Great

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Contact Information

Reach out through any of the channels below. I typically respond within 24 hours.