Abdulrahman Al-Halqi
AI Systems Engineer
I design and ship production-ready AI applications across LLM systems, computer vision, automation, ML deployment, APIs, and full-stack product engineering.
LLM Systems
Computer Vision
MLOps
AI APIs
Project Execution System
Every project moves through a clear engineering pipeline from research and planning to deployment, monitoring, and iteration.
Research
Understand the domain
Planning
Define scope and risks
System Design
Map architecture
Data / Integration
Connect inputs and APIs
Model Training
Tune and validate
Backend + APIs
Build reliable services
Interface
Design the product layer
Deployment
Ship to production
Monitoring
Measure and improve
Final Output
Production Ready
A complete intelligent system with model logic, APIs, interface, deployment path, and feedback loop.
I connect applied AI research, backend infrastructure, product-grade interfaces, and deployment workflows into complete intelligent systems.
4+
Years building
20+
AI / web systems
10+
Research builds
System Architecture
Research
Model selection
Engineering
APIs + orchestration
Deployment
Cloud / edge runtime
Technical clusters across model work, system design, deployment, APIs, and product-grade interfaces.
Roles and engagements mapped through systems shipped, technical scope, and product impact.
Certifications and learning signals connected to production engineering skills.
Meta / Coursera
Oracle Academy
Meta / Coursera
Meta / Coursera
Ibb University - Center for Computer & IT
A systems-engineer roadmap from foundations to production AI architectures.
Ibb University - Center for Computer & IT
Completed the IUCDL program and built a strong foundation in information technology, computer basics, Windows, internet tools, and Microsoft Office applications. This stage helped me become comfortable with digital tools and prepared me for deeper technical learning.
Self-Learning & Online Courses
Focused on learning programming fundamentals, Python, SQL, database design, and relational database management. This stage helped me understand how software systems store, process, and organize data.
Scramblebit Pazarlama Programlama
Completed an onsite internship focused on machine learning, deep learning, and natural language processing. I learned the core concepts behind AI models, trained models, tested their performance, and gained practical exposure to the AI development workflow.
Independent Projects
Began building practical computer vision systems that connected AI models with real-world inputs and hardware. Projects included gesture-based LED control, robot arm control, OCR field extraction, face recognition, and real-time visual interaction systems.
Independent Projects
Moved beyond standalone models by integrating AI systems into complete applications with frontend interfaces, backend APIs, databases, real-time communication, and hardware components. This stage shaped my direction as an AI engineer who can turn models into usable products.
Karabük University
Completed a research-focused internship under Doç. Dr. Emrullah SONUÇ, studying federated learning and decentralized machine learning. I worked with Hugging Face datasets and trained models using the federated learning concept, gaining experience in privacy-aware distributed AI systems.
Independent Projects
Developed projects combining computer vision, NLP, speech transcription, and LLM-based summarization. This included speaker detection using MediaPipe Face Mesh, Whisper-based transcription, LangChain summarization, YouTube video processing, and PDF summarization workflows.
Ultimate Solution
Started working as an AI Engineering Intern, focusing on adding AI models, chatbots, and intelligent automation features to business systems, accounting systems, and personal management platforms built with .NET and web technologies.
Personal Brand
Started building a professional portfolio platform to present projects, services, experience, skills, and future AI demos. The goal is to position myself as an AI engineer who combines machine learning, computer vision, LLMs, backend systems, frontend development, and real-world product integration.
Short updates on projects, learning, certificates, events, and technical progress.
I’m excited to share that I have obtained the Innovating with Google Cloud AI certification, strengthening my understanding of AI solutions, cloud-based machine learning, and Google Cloud’s AI ecosystem.
Read moreTÜBITAK 2209-A University Students Research Projects Support Program.
Read moreSelected work framed around problem, architecture, model/system stack, deployment path, and product outcome.
This project explores the connection between computer vision and robotics by implementing a robot arm that can be controlled through visual hand-based input. Instead of relying only on traditional controllers, the system uses vision-based detection to interpret user movement and convert it into physical robotic motion. The project helped build practical experience in robotics, hardware control, and real-time AI interaction.
Problem
Architecture
Deploy
This project is a complete intelligent parking management system that combines computer vision, OCR, embedded systems, drone monitoring, and full-stack web technologies. The system detects and recognizes vehicle license plates, monitors parking slot availability in real time, controls a physical gate using hardware components, and displays live system updates through a React-based interface connected to a FastAPI backend.
Problem
Architecture
Deploy
This project is a multimodal AI system focused on speaker detection, tracking, speech segmentation, transcription, and summarization. It uses MediaPipe Face Mesh to analyze facial landmarks and determine whether a detected person is speaking based on lip movement ratios. The system can identify active speakers in video, segment speaker-specific audio, transcribe speech using Whisper, and generate concise summaries using LangChain. It also supports YouTube videos and PDF documents, allowing users to extract and summarize content from multiple sources.
Problem
Architecture
Deploy
This project focuses on extracting structured information from card images using image processing, OCR, and regular expressions. The system processes images of ID cards, bank cards, and similar field-based cards, extracts visible text, and organizes the detected data into meaningful fields. It demonstrates how OCR can be used as a practical automation layer for document understanding and data entry workflows.
Problem
Architecture
Deploy
This project demonstrates the integration of computer vision with embedded hardware by allowing LED flashes to be controlled using hand gestures. The system uses MediaPipe’s pre-trained hand detection model to recognize hand signals in real time, then sends the detected commands to an Arduino microcontroller responsible for controlling the LEDs. It was built as a practical exploration of how AI-powered perception can interact with physical electronic components.
Problem
Architecture
Deploy
This project demonstrates how a face recognition model can be integrated into a real-world attendance application. The system uses a React interface connected to a FastAPI backend to process live frames, verify user identity, and detect whether the presented face is real or spoofed. The prototype highlights how face recognition and anti-spoofing can work together to create a more secure and efficient attendance workflow.
Problem
Architecture
Deploy
LLM products, computer-vision workflows, automation platforms, AI integrations, dashboards, APIs, and deployment-ready ML systems.
End-to-end development of intelligent systems that transform machine learning models into practical, production-ready applications. This service focuses on building AI-powered solutions that solve real business and engineering problems, from model integration to backend APIs and user-facing interfaces.
Design and development of intelligent chatbot and LLM-based systems using modern language model tools. This includes building assistants, retrieval-based systems, document summarizers, and AI workflows that can understand, process, and generate useful responses from structured or unstructured data.
Development of computer vision systems for detection, recognition, tracking, OCR, automation, and real-time visual intelligence. This service is ideal for projects involving cameras, image processing, video analysis, object detection, face recognition, and visual monitoring.
Building complete AI applications that combine frontend interfaces, backend APIs, databases, authentication, real-time communication, and AI models. This service turns prototypes and machine learning ideas into complete usable products.
Deployment and integration of machine learning models into backend systems, APIs, dashboards, and production-style workflows. This service helps move models from notebooks or scripts into usable applications that can receive inputs, return predictions, and support real users.
Integration of AI and computer vision with physical hardware systems such as Arduino, ESP32-CAM, servo motors, sensors, displays, and robotics components. This service focuses on connecting intelligent software with real-world devices.
Development of automation systems that use OCR, image processing, text extraction, and intelligent parsing to reduce manual work. This service is useful for extracting data from cards, forms, documents, images, and repetitive business workflows.
Rapid development of technical prototypes and MVPs for AI, computer vision, robotics, automation, and full-stack software ideas. This service helps convert early concepts into working demos that can be tested, presented, or improved into full products.
Share the model, workflow, automation, or platform you want to build. I'll respond with a practical engineering path.
I'm open to freelance projects, consulting, and full-time opportunities. Response time: within 24 hours.
alhalqiabdulrahman@gmail.com
abdulrahman-alhalqi-88028429a
GitHub
Halaqi
Prefer a quick call? Request a meeting via email.
