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Sharjeel Sajid

NUST · 2026

sharjeelsajid09@gmail.com

Phone

923403750694

https://www.linkedin.com/in/sharjeel-sajid-7b953b289/

GitHub

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Academic

Program

Software Engineering

CGPA

3.34

Year

2026

Education

SEECS

Address

18-G 202 P.O.F , Wah cantt , Pakistan

DOB

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Career

Current role

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Target role

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Skills

C++, Rust, Python, JavaScript, React.js, Next.js, FastAPI, Microservices Architecture, Deep Learning, Computer Vision, Generative AI, Google Cloud Platform, Docker, Cloud Functions, DORA-RS, ESP32, Embedded Systems, IoT Sensors & Automation, LeetCode

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.

Sharjeel Sajid
Cell: 923403750694 | Email: sharjeelsajid09@gmail.com
LinkedIn: https://www.linkedin.com/in/sharjeel-sajid-7b953b289/
Address: 18-G 202 P.O.F , Wah cantt , Pakistan
PROFESSIONAL PROFILE
Final-year Software Engineering undergraduate at NUST (Class of 2026) with a strong foundation in algorithmic problem-solving,
demonstrated by solving over 400 LeetCode questions. Passionate about Robotics, AI, and Open Source, with practical experience
contributing to the DORA-RS robotic operating system and competing as a global ﬁnalist in the JAXA Kibo Robot Programming
Challenge. Proﬁcient in C++, Rust, and Cloud Microservices, with a proven track record of engineering high-performance solutions,
including a custom search engine and adversarial deepfake disruption models.
EDUCATION
Software Engineering
SEECS , Islamabad , 3.34 (2026)
INTERNSHIP EXPERIENCE
Research Intern | MachVis Labs, NUST
01-Jun-2024 - 31-Aug-2024
Initiated internship by building a strong foundation in Deep Learning (DL) and Computer Vision (CV) concepts. Conducted research
on "Neural Architecture Search (NAS) on Entity Recognition" to explore optimal model architectures. Utilized the NNI (Neural
Network Intelligence) library to experiment with and identify optimal architectures for the assigned research problem.
FINAL YEAR PROJECT
Adversarial Disruption of Deepfake Generation Models
Developing a system to disrupt deepfake generation by implementing adversarial attacks on generative models. Engineering
imperceptible perturbations—subtle noise added to source images that remains invisible to the human eye but prevents models from
generating realistic deepfakes. Targeting state-of-the-art diﬀusion models, including InstructPix2Pix and Stable Diﬀusion 1.5, by
analyzing their specific architectures to design effective disruption mechanisms.
TECHNICAL EXPERTISE
Programming Languages
C++, Rust, Python, JavaScript.
Web Development & Frameworks
React.js, Next.js, FastAPI, Microservices Architecture.
Artificial Intelligence
Deep Learning (DL), Computer Vision (CV), Generative AI
Cloud & DevOps
Google Cloud Platform (GCP), Docker, Cloud Functions.
Robotics & IoT
DORA-RS (Robotic Operating System), ESP32, Embedded Systems, IoT Sensors & Automation.

AI enrichment

Sharjeel Sajid is a final-year Software Engineering undergraduate at NUST with a 3.34 CGPA, specializing in AI, robotics, and full-stack development. He has practical experience in deep learning research, adversarial machine learning, and contributing to open-source robotic systems.

Skills (AI)

["C++", "Rust", "Python", "JavaScript", "React.js", "Next.js", "FastAPI", "Deep Learning", "Computer Vision", "Generative AI", "GCP", "Docker", "Robotics", "LeetCode"]

Status: ai_done

Provenance

Source file: SEECS - Software Engineering-2026(1).pdf
From job #260 page 93
Created: 1778138736