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Muhammad Ammar Bin Akram

NUST · 2022

ammarbinakram16@gmail.com

Phone

923345511535

https://www.linkedin.com/in/muhammad-ammar-bin-akram-619b05257/

GitHub

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Academic

Program

Bachelors of Science in Computer Science

CGPA

3.35

Year

2022

Education

School of Electrical Engineering and Computer Science

Address

HOUSE NO D/90 SECTOR 4/B KHAYABAN E SIRSYED RAWALPINDI , Rawalpindi , Pakistan

DOB

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Career

Current role

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Target role

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Skills

Python, OpenCV, PyTorch, TensorFlow, YOLO, Computer Vision, Deep Learning, scikit-learn, numpy, pandas, Edge Devices, Raspberry Pi, LLM, RAG

Verbatim text

The exact text the LLM saw on the page (or the booklet text from the old import). This is what powers semantic search.

Muhammad Ammar Bin Akram
Cell: 923345511535 |  Email: ammarbinakram16@gmail.com
LinkedIn: https://www.linkedin.com/in/muhammad-ammar-bin-akram-619b05257/
Address: HOUSE NO D/90 SECTOR 4/B KHAYABAN E SIRSYED RAWALPINDI , Rawalpindi , Pakistan
PROFESSIONAL PROFILE
Aspiring computer vision and deep learning engineer with hands-on experience in developing AI systems for real-world applications,
including automated snooker scoring and mango detection. Skilled in Python, OpenCV, PyTorch, TensorFlow, and YOLO, with
experience in deploying models on edge devices like Raspberry Pi. Seeking opportunities to apply technical expertise to innovative
projects in computer vision and AI, while continuing to grow in advanced deep learning and real-time system development.
EDUCATION
Bachelors of Science in Computer Science
School of Electrical Engineering and Computer Science , Islamabad , 3.35 (2022)
INTERNSHIP EXPERIENCE
DataQuartz
23-Jun-2025 - 18-Aug-2025
Worked on the Snooker Vision Project, focused on automating the complete snooker game using deep learning and computer vision,
Developed features for player turn management and real-time score calculation, Implemented foul detection logic based on ball
interactions and shot analysis , Designed potting detection mechanisms for accurate game event tracking
RoadGauge
11-Aug-2025 - 31-Oct-2025
Worked on tracking road assets using advanced computer vision models, Contributed to the development of an automated road
inspection system, Applied 2D image processing techniques for visual analysis of road conditions, Utilized deep learning models to
improve detection and tracking accuracy
FINAL YEAR PROJECT
ExportEdge AI
Designed and implemented an edge-AI solution to automate mango quality inspection and export decision-making. The system uses
deeplearning for mango classiﬁcation and disease segmentation, combined with an LLM (RAG-based) to recommend optimal export
prices and destination countries, deployed on Raspberry Pi.
TECHNICAL EXPERTISE
Computer vision and Deep learning
Experienced in designing and implementing computer vision systems for real-world applications, including object detection, image
classiﬁcation and segmentation, tracking, and automated inspection. Skilled in using Python, scikit-learn, numpy, pandas, OpenCV, PyTorch, TensorFlow, and YOLO fro developing AI so ...

AI enrichment

Muhammad Ammar Bin Akram is a Computer Science graduate with a focus on computer vision and deep learning, possessing internship experience in automated game scoring and road asset tracking. He has practical expertise in deploying AI models on edge devices and integrating large language models for decision support systems.

Skills (AI)

["Python", "Computer Vision", "Deep Learning", "OpenCV", "PyTorch", "TensorFlow", "YOLO", "Edge AI", "Raspberry Pi", "Object Detection", "Image Segmentation", "LLM Integration", "RAG"]

Status: ai_done

Provenance

Source file: SEECS - Computer Science-2026.pdf
From job #258 page 102
Created: 1778167261