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Md Adnan T. Python, Docker and AI Platforms

My name is Md Adnan T. and I have over 5 years of experience in the tech industry. I specialize in the following technologies: PyTorch, Python, Keras, Deep Learning, Computer Vision, etc.. I hold a degree in Bachelor of Computer Science (BCompSc). Some of the notable projects I've worked on include: Computer Vision Based Evaluation for Eczema Patients, Computer Aided Gastrointestinal Disease Segmentation, Segmentation model showcases, People Detection, Tracking, and Counting in Video, Product Tracking in Supershop Shelf, etc.. I am based in Dhaka, Bangladesh. I've successfully completed 8 projects while developing at Softaims.

Information integrity and application security are my highest priorities in development. I implement robust validation, encryption, and authorization mechanisms to protect sensitive data and ensure compliance. I am experienced in identifying and mitigating common security vulnerabilities in both new and existing applications.

My work methodology involves rigorous testing—at the unit, integration, and security levels—to guarantee the stability and trustworthiness of the solutions I build. At Softaims, this dedication to security forms the basis for client trust and platform reliability.

I consistently monitor and improve system performance, utilizing metrics to drive optimization efforts. I'm motivated by the challenge of creating ultra-reliable systems that safeguard client assets and user data.

Main technologies

  • Python, Docker and AI Platforms

    5 years

  • PyTorch

    4 Years

  • Python

    1 Year

  • Keras

    2 Years

Additional skills

Direct hire

Potentially possible

Previous Company

Polyfins Technology Inc

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Experience Highlights

Computer Vision Based Evaluation for Eczema Patients

A novel toolmade by combiningmultiple different image classification, image segmentation and multi‐task deep learningmodels to detect, classify and evaluate the condition and disease progression in Ec

Computer Aided Gastrointestinal Disease Segmentation

In this project, I did an experiment to finetune Unet++ based model to get the best segmentation metrics on KVASIR segmentation dataset for GI tract disease detection and segmentation. We trained the

Segmentation model showcases

Showcase of multiple segmentation models running on cpu to demonstrate model optimization and preserving accuracy

People Detection, Tracking, and Counting in Video

• Using a pre‐trained YOLO11 model to detect, track, and count people in a specified region of interest (ROI) within a video feed. • A specific area in the frame is defined as the region of interest,

Product Tracking in Supershop Shelf

• Using a finetuned YOLO12 model to detect, and count specific product in shelf and restock alert in case of low product.. • Finetune YOLO model to detect custom products, then use the model to get co

Education

  • Ahsanullah University of Science and Technology

    Bachelor of Computer Science (BCompSc) in Computer science and engineering

    2014-01-01-2018-01-01

Languages

  • Bengali (Native or Bilingual)
  • English (Fluent)