Muhammad Atif

Academic

Career

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Muhammad Atif
Cell: 03425103868 |  Email: atifbalti984782@gmail.com
LinkedIn: https://www.linkedin.com/in/muhammad-atif-15451b273/
Address: Marafie Colony, Satellite Town, Sadpara Road , Skardu , Pakistan
PROFESSIONAL PROFILE
A final-year electrical engineering student with hands-on experience in UAV systems engineering, flight-control design, and
embedded systems integration. Proven ability to work on fixed-wing VTOL platforms, including propulsion system development and
autopilot configuration. Strong foundation in control systems, avionics, and real-time embedded programming, with practical
experience in flight testing, data analysis, and system validation. Highly motivated engineer seeking graduate-level roles in UAV
systems, embedded systems, flight-control, or avionics engineering.
EDUCATION
Bachelor of Electrical Engineering
School of Electrical Engineering and Computer Science (SEECS) , Islamabad , 2.13 (2026)
INTERNSHIP EXPERIENCE
Dronext Pvt. Ltd (SEECS)
01-Jul-2025 - 08-Sep-2025
During this internship, I worked on the design, integration, and testing of a fixed-wing VTOL UAV, contributing to propulsion
architecture, avionics integration, and flight-control systems. I developed and integrated a hybrid propulsion system combining
vertical-lift and forward-thrust motors and supported system-level performance validation. I implemented flight-control algorithms and
autopilot configurations, including VTOL transition control mixing and PID tuning to ensure stability and smooth mode transitions.
Additionally, I conducted bench testing and flight trials, analyzed flight telemetry data, and iteratively refined control laws to improve
flight performance, reliability, and robustness.
FINAL YEAR PROJECT
Ababeel: An AI-Driven Swarming Drone Network
This project proposes the development of IntelliSwarm, an intelligent and adaptive swarm of autonomous drones designed to operate
in dynamic environments with decentralized control. The system features a hierarchical yet flexible architecture where a primary
master drone coordinates multiple slave drones, with built-in capability for any slave to seamlessly assume the master role upon
failure detection. The swarm will be built using cost-effective hardware, with a focus on modular integration, real-time communication,
and fault-tolerant behavior. Machine Learning (ML) algorithms will be employed for data-driven decision-making, environmental
awareness, and adaptive mission execution. The project aims to design, simulate, and prototype a robust drone swarm with scalable,
self-healing communication protocols and AI-enhanced autonomy, suitable for applications in surveillance, search-and-rescue, and
environmental monitoring.
TECHNICAL EXPERTISE
UAV Systems Engineering
Hands-on experience in the design, integration, and testing of fixed-wing VTOL UAVs, covering propulsion architecture, avionics
integration, and system-level validation.
Flight Control & Autopilot Systems
Implemented and tuned flight-control algorithms, including VTOL transition control mixing and PID control, using embedded autopilot
stacks for stable and reliable flight.
UAV Testing & Validation

AI enrichment