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  • Pakistan Air Force (Strategic IT Operations)
  • Pakistan
  • 08:19 (UTC +05:00)
  • LinkedIn in/qaismubeen

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qaismubeen/README.md

Qais Mubeen

IT Operations professional with 4+ years of service in the Pakistan Air Force, working in high-security, mission-critical environments. Special Forces trained and ranked among the top 100 personnel.

I am building a career in AI engineering. My focus is Machine learning and Deep learning, and the Engineering needed to turn models into reliable, deployed systems. I pair every course with a working project and write down what broke and why.

Background

  • Server Operations Room: maintained mission-critical server infrastructure with 24/7 uptime and performance monitoring.
  • Data Transfer Sections: secure data handling, system-to-system transfers and archival of sensitive information.
  • Maintenance Labs: diagnosed and resolved hardware, software and networking faults.
  • Surveillance Operations: operated sensitive surveillance systems where precision and confidentiality were required.
  • Servers Configuration and IT Support: performance tuning, troubleshooting and system health monitoring.
  • Special Forces: completed training and served as a Special Forces professional.

Focus

  • Machine learning and deep learning: recommendation systems, attention-based image captioning, regression and graph-based modelling
  • Python, PyTorch, TensorFlow and Keras for modelling and data analysis; C++ for systems-level programming
  • Linux, cloud (AWS, Azure) and DevOps fundamentals for deploying and serving models
  • Cybersecurity as a secondary interest

Training

  • Machine Learning Specialization (DeepLearning.AI and Stanford Online, Coursera)
  • Deep Learning Specialization (DeepLearning.AI, Coursera)
  • Artificial Intelligence Using Python (DigiSkills.pk)
  • IBM (Coursera): Machine Learning with Python; Introduction to Deep Learning and Neural Networks with Keras; Deep Learning with Keras and TensorFlow; Introduction to Neural Networks and PyTorch; AI Capstone Project with Deep Learning
  • AI Engineering
  • RAG and Agentic AI
  • Natural Language Processing

Selected Projects

  • image-captioning-attention: image captioning with an attention mechanism
  • ai-adaptive-recommender: adaptive recommender combining K-Means clustering, supervised learning, SVD matrix factorization and a multi-armed bandit, evaluated on MovieLens 100K
  • traffic-flow-analysis: multivariable traffic flow modelling on a real 30-day dataset
  • price-comovement-network: graph analysis of CPI co-movement across Pakistani cities, 2022 to 2024
  • cache-optimization: calculus-based optimization of multi-level cache allocation
  • UniVault: C++ record management and simulation system with dynamic memory and file persistence

Contact

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  1. ai-adaptive-recommender ai-adaptive-recommender Public

    Adaptive recommender on MovieLens 100K: K-Means segmentation, SVD matrix factorization, supervised preference models and a multi-armed bandit, with a Streamlit app

    Jupyter Notebook

  2. cache-optimization cache-optimization Public

    Mathematical optimization of multi-level cache allocation using calculus and Python — derives minimum memory access time through symbolic differentiation, numerical solving, and visualization.

    Python

  3. image-captioning-attention image-captioning-attention Public

    Image captioning model using an attention mechanism

    Jupyter Notebook

  4. price-comovement-network price-comovement-network Public

    Graph-based analysis of consumer price co-movement across Pakistani cities using CPI data, cosine similarity, and NetworkX centrality measures — tracking economic relationships from 2022 to 2024.

    Python

  5. traffic-flow-analysis traffic-flow-analysis Public

    Multivariable traffic flow modeling using linear regression, gradient analysis, directional derivatives, triple integration, and city-wise comparison — built with Python and real 30-day dataset.

    Python

  6. UniVault UniVault Public

    Console-based C++ system for record management and simulation built with heap-allocated 3D grid architecture, dynamic memory, pointer arithmetic, and file persistence.

    C++