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Muhammad A. Cloud, AI and Backend Platforms

My name is Muhammad A. and I have over 3 years of experience in the tech industry. I specialize in the following technologies: Python Script, PyTorch, Machine Learning, Azure DevOps, Azure Machine Learning, etc.. I hold a degree in Bachelor of Science (BS). Some of the notable projects I've worked on include: Serverless Runpod deployment of Video-to-video workflow, Forest Damage Detection using Unsupervised Learning and Sent-2 Imagery, Finetuning Stable Diffusion using Dreambooth LoRA finetuning, ML on ESP32 CAM. I am based in Islamabad, Pakistan. I've successfully completed 4 projects while developing at Softaims.

My expertise lies in deeply understanding and optimizing solution performance. I have a proven ability to profile systems, analyze data access methods, and implement caching strategies that dramatically reduce latency and improve responsiveness under load. I turn slow systems into high-speed performers.

I focus on writing highly efficient, clean, and well-documented code that minimizes resource consumption without sacrificing functionality. This dedication to efficiency is how I contribute measurable value to Softaims' clients by reducing infrastructure costs and improving user satisfaction.

I approach every project with a critical eye for potential bottlenecks, proactively designing systems that are efficient from the ground up. I am committed to delivering software that sets the standard for speed and reliability.

Main technologies

  • Cloud, AI and Backend Platforms

    3 years

  • Python Script

    2 Years

  • PyTorch

    1 Year

  • Machine Learning

    1 Year

Additional skills

  • Python Script
  • PyTorch
  • Machine Learning
  • Azure DevOps
  • Azure Machine Learning
  • Azure OpenAI Service
  • MERN Stack
  • FastAPI
  • AWS Lambda
  • AWS Development
  • Django
  • Stable Diffusion
  • OpenAI API
  • LoRa
  • Serverless Computing

Direct hire

Potentially possible

Previous Company

Resilienttech- MobiPixels

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Experience Highlights

Serverless Runpod deployment of Video-to-video workflow

Deployed a complex video generation pipeline powered by WanVideo diffusion models using serverless infrastructure on RunPod and AWS. The system included T5 and CLIP encoders, video-text conditioning,

Forest Damage Detection using Unsupervised Learning and Sent-2 Imagery

I am presenting the demo of my final year project on Forest Damage Detection using Unsupervised Learning. I have developed a web portal where you can see the results of our project. We collected groun

ML on ESP32 CAM

The goal was to train a Machine Learning algorithm and run it on the esp32 cam module. ML algorithms are heavy in size and require a lot of computational power. So the task was to optimize them so the

Education

  • National University of Sciences and Technology

    Bachelor of Science (BS) in Computer science

    2019-01-01-2023-01-01

Languages

  • English (Native or Bilingual)