Hi, I’m Muhammad Amir Khan, a Senior CRM and AI Engineer with 10+ years of international remote experience across the UAE, USA, UK, Cyprus, Germany, and Canada. I specialize in SuiteCRM/SugarCRM development, custom modules, workflow automation, API integrations, and performance optimization.
I also architect AI/LLM-powered solutions using tools like OpenAI GPT-4, Grok, LangChain, and RAG to automate complex processes and unlock insights. I’m actively seeking a Senior CRM/AI Engineer role in Saudi Arabia and ready to relocate from Lahore, Pakistan.
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Developed a comprehensive parking management platform for a mid-sized logistics company in the UK, handling over 50 parking lots across multiple cities. The system allowed real-time tracking of parking spots, automated bookings, and predictive availability forecasting using machine learning to reduce no-show rates by 35%. Integrated user portals for admins, employees, and customers, with features like QR code entry, dynamic pricing based on demand, and automated notifications via WhatsApp. Custom modules were built for lot management, spot allocation, vehicle registration, and booking workflows, ensuring seamless scalability for 10,000+ daily transactions.
Key Features:
• Real-time spot availability dashboard with Google Maps integration for location-based searches.
• AI-driven prediction model using Python to forecast occupancy trends based on historical data, time of day, and events.
• Secure payment processing via Stripe and PayPal, with automated refunds for cancellations.
• Custom reporting tools for revenue analytics and usage patterns.
Technologies Used: SuiteCRM 8 (core framework with custom modules, logic hooks, and workflows), PHP 8, Laravel for backend APIs, Python (Pandas, Scikit-learn for ML predictions), JavaScript (React for frontend portals), MySQL, Google Maps API, Stripe API, WhatsApp Business API, Docker for deployment.
Outcomes: Deployed in 3 months, resulting in a 40% increase in booking efficiency and £150,000 annual savings in operational costs. The system handled peak loads of 5,000 users without downtime, and received positive feedback for its intuitive UI
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