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Vladislav Li

Profile

MLOps and Systems Engineer with a PhD in Artificial Intelligence, specialising in deploying and scaling machine learning systems in production environments. Experienced in building distributed backend services, containerised applications, and real-time inference pipelines. Strong background in Linux systems, Docker-based infrastructure, and GPU-enabled workloads across cloud and on-premise environments.

Key Skills

MLOps & DevOps - Docker, containerised deployments, distributed systems - CI/CD (GitHub Actions), automated workflows - Kubernetes (basic deployment and orchestration) - ML model deployment and inference pipelines

Backend & Systems - FastAPI, Celery, Redis (asynchronous and distributed processing) - REST APIs, microservices architecture - Linux (Ubuntu, Arch, Fedora, Void) - GPU workloads and performance optimisation

Cloud & Infrastructure - AWS (EC2 deployment, service configuration) - Azure (AI services integration) - Networking fundamentals (switches, cabling, system setup) - Server and workstation hardware configuration

Programming - Python (backend systems, ML deployment) - C/C++ (performance-critical systems) - C# (Unity development) - SQL (database design and normalisation)

Professional Experience

Researcher (AI Systems / MLOps Focus)

Kingston University, London | 2021 – Present

  • Designed and deployed containerised ML systems using Docker across Linux environments
  • Built distributed inference pipelines using FastAPI, Celery, and Redis for asynchronous processing
  • Developed production-oriented backend services for scalable AI workloads
  • Optimised GPU-based inference performance for real-time applications
  • Integrated ML systems into simulation and XR environments for industrial use cases
  • Contributed to EU-funded projects focused on reliable and scalable AI deployment in Industry 4.0

Projects

ML Deployment Platform (Distributed System)

  • Designed a scalable inference platform using FastAPI, Celery, and Redis
  • Implemented asynchronous task queues and distributed processing
  • Containerised services for reproducible deployment

Cloud & Service Deployment

  • Deployed services on AWS (EC2) for remote access and hosting
  • Integrated Azure AI services into production workflows

Containerised AI Systems

  • Built Docker-based ML services for real-time inference
  • Managed dependencies and runtime environments across Linux systems

Education

PhD in Artificial Intelligence
Kingston University | 2021 – 2024

MSc Games Development (Programming)
Kingston University | 2019 – 2020

BSc (Hons) Computer Science (Games Programming)
Kingston University | 2015 – 2019

Publications

Available on request (focused on computer vision and efficient AI systems)

Additional

  • Experience with VR/AR systems (Unity, Unreal Engine, HoloLens)
  • Exposure to robotics systems (Unitree platform, Python-based control)