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📄 Curriculum Vitae

🚀 Profile

Machine Learning Engineer with a PhD in Artificial Intelligence, specialising in computer vision, efficient AI, and real-time systems. Experienced in designing and deploying scalable AI solutions, optimising deep learning models for performance, and building production-ready backend systems. Strong background in C++, Python, and GPU-based workloads, with additional expertise in VR/AR and simulation environments.

🧠 Core Skills

Machine Learning & AI - Computer Vision (object detection, few-shot learning, model optimisation) - PyTorch, TensorFlow/Keras, NumPy, Pandas, scikit-learn - Real-time inference and edge AI systems - Data pipelines and model evaluation

Backend & Systems - FastAPI, Celery, Redis (distributed task systems) - REST APIs and asynchronous processing - Docker, containerised ML deployments - Linux (Ubuntu, Arch, Void)

Programming - Python (ML systems, backend services) - C/C++ (high-performance systems, graphics, game engines) - C# (Unity development) - JavaScript, HTML, CSS, PHP

Graphics / XR - Unity, Unreal Engine 4 - OpenGL, Vulkan - VR/AR (Oculus, HTC Vive, HoloLens)

💼 Professional Experience

Researcher (AI / Computer Vision)

Kingston University, London | 2021 – Present

  • Designed and optimised computer vision models for object detection under data-scarce conditions
  • Improved model efficiency and inference performance for real-time and resource-constrained environments
  • Built scalable AI pipelines using FastAPI, Celery, and Redis for distributed processing
  • Developed and deployed containerised ML systems using Docker across Linux environments
  • Collaborated on industry-aligned EU projects focused on AI reliability and industrial optimisation
  • Worked with synthetic data and simulation environments to enhance model robustness

🛠 Selected Projects

AI Model Deployment Platform

  • Built a distributed ML system using FastAPI + Celery + Redis
  • Enabled asynchronous task execution and scalable inference pipelines
  • Containerised services for reproducibility and deployment

Real-Time Object Detection Systems

  • Developed and optimised deep learning models for real-time applications
  • Reduced computational cost while maintaining detection accuracy
  • Applied techniques for few-shot learning and data efficiency

VR/AR AI Applications

  • Integrated computer vision models into Unity-based environments
  • Built interactive AR/VR prototypes using real-time object recognition

Game Engine & Graphics Projects

  • Developed systems using OpenGL and Vulkan
  • Built gameplay and engine features in Unity and Unreal Engine
  • Experience with console-level development (PS4 environment)

🎓 Education

  • Ph.D. in Artificial Intelligence
    Kingston University (2021 – 2024)

  • M.Sc. in Games Development (Games Programming)
    Kingston University (2019 – 2020)

  • B.Sc. with Honours in Computer Science (Games Programming)
    Kingston University (2015 – 2019)

📄 Selected Publications

  • Enhancing 3D Object Detection in Autonomous Vehicles (2025)
  • Energy Efficiency of Few-Shot Learning (2024)
  • Data Augmentation for Low-Shot Object Detection (2024)

(Full publication list available on request)

🌍 Projects & Collaboration

RAIDO (EU Project) | 2023 – Present
- AI reliability and data optimisation for industrial applications

TALON (EU Project) | 2022 – 2025
- AI-driven optimisation for Industry 4.0 systems

🎮 Additional Experience

  • Multiple game jam awards, including 1st place finishes
  • Developed prototypes across PC, mobile, and experimental platforms
  • Built custom tools and small-scale engines

🔑 Keywords

Machine Learning, Computer Vision, PyTorch, TensorFlow, FastAPI, Docker, Linux,
C++, Python, Real-Time Systems, AI Deployment, Edge AI, VR/AR, Unity, Unreal Engine