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My name is Aadil Jaleel C. and I have over 11 years of experience in the tech industry. I specialize in the following technologies: Computer Vision, TensorFlow, Python, C++, Robot Operating System, etc.. I hold a degree in Master of Science (MS), Bachelor of Engineering (BEng). Some of the notable projects I’ve worked on include: [AI / LLM / GenAI] Qazi.ai - Llama 3.1 based Legal Case Management, [AI / Qt ] Nvidia Deepstream AI Pipeline with Qt for Jetson Orin, [Embedded Linux / Hardware / C++ / FreeRTOS / STM32] Handheld Device, [C++ / ROS / Qt] Sensor Data Decoding & Visualization, [AI / GPT API] Automatic Comparison of PDF with Audio in Danish, etc.. I am based in Islamabad, Pakistan. I've successfully completed 14 projects while developing at Softaims.
I am a business-driven professional; my technical decisions are consistently guided by the principle of maximizing business value and achieving measurable ROI for the client. I view technical expertise as a tool for creating competitive advantages and solving commercial problems, not just as a technical exercise.
I actively participate in defining key performance indicators (KPIs) and ensuring that the features I build directly contribute to improving those metrics. My commitment to Softaims is to deliver solutions that are not only technically excellent but also strategically impactful.
I maintain a strong focus on the end-goal: delivering a product that solves a genuine market need. I am committed to a development cycle that is fast, focused, and aligned with the ultimate success of the client's business.
Main technologies
AI, Machine Learning and Embedded Systems
11 years
Computer Vision
6 Years
TensorFlow
1 Year
Python
5 Years
Additional skills
- Computer Vision
- TensorFlow
- Python
- C++
- Robot Operating System
- Image Processing
- MATLAB
- Linux
- Embedded Linux
- Keras
- Machine Learning
- Automotive Engineering
- TCP/IP
- DevOps
- Qt Framework
Direct hire
Potentially possible
Previous Company
Techlogix
Experience Highlights
[AI / LLM / GenAI] Qazi.ai - Llama 3.1 based Legal Case Management
* Legal Document Generation (e.g, Summons, Petitions, Draft Judgement) * Legal Document Analysis (e.g., Arguments, Precedence) * End-to-End Case Management
[AI / Qt ] Nvidia Deepstream AI Pipeline with Qt for Jetson Orin
The goal was to * Training AI Model per user requirements * Optimizing and Deploying the AI Model on Nvidia Jetson Orin * Implement Tracking Algorithm * Picture-in-Picture Replay * Integration of Deepstream with Qt frontend (C++)
[Embedded Linux / Hardware / C++ / FreeRTOS / STM32] Handheld Device
Problem: Require a rich GUI but real-time sensor data acquisition. Solution: A multi-core embedded system running Linux and FreeRTOS simultaneously. Hardware: OSD32MP1-BRK with touchscreen and touch controller directly interfaced. Schematics and PCB Design is covered by NDA. Software: The exact details of the project are subject to an NDA, but the following technologies were used: 1. FreeRTOS running on cortex M4 on Octavo OSD having STM32MP157. The FreeRTOS code handled obtained real time data from a proprietary sensor. The data was then time stamped and sent to the A7 core over IPCC. The whole toolchain was optimized for real-time data communication from sensor to A7 2. Cortex A7 coprocessor ran OpenSTLinux. A GUI application was made using Qt6. 3. Crystalfontz 2.4" LCD (CFAF240320A0-024SC) was integrated directly with OpenSTLinux via SPI and slightly modified linux kernel drivers and device tree. 4. Similarly the touch controller from CFAF240320A0-024SC was integrated with I2C kernel drivers. 5. The proprietary sensor was interfaced using SPI on Cortex-M4. 6. Device tree and kernel customizations were done to optimize OSTLinux for product usecase 7. The Qt Application featured, multiple screens, with charts for sensor data, provision to record data and to display live data 8. The Qt application and also allowed for CSV export of recorded sensor data and backup to PC via USB. 9. Django application was also deployed on A7 for device configuration to download data and upload settings.
[C++ / ROS / Qt] Sensor Data Decoding & Visualization
Automotive Bus Data / Sensor data playback, decoding and visualization * development of playback for mdf4/mf4, dat and rosbag files in C++ * development of data decoding (CAN, CANFD, SomeIP, TAPI with DBC, FIBEX, ARXML databases) lib in C++ * development of data flows in in ROS * development of video data decoder and post processing in CUDA * Visualization of data (video, LIDAR, etc) in RViz
[AI / GPT API] Automatic Comparison of PDF with Audio in Danish
Given an audio sample and a PDF, the goal was to compare them and find any discrepancies. The project was aimed at Danish language. Audio samples were converted to text using Google Speech to Text API, and then corrected for artifacts using GPT4 API. PDF was converted and calibrated using various techniques including GPT4 API. Off-the-shelf Whisper API struggled with Danish but fine-tuning could produce results comparable to Google STT API. Image for Illustration.
Education
Georgia Institute of Technology
Master of Science (MS) in Computer Science - Specialization in Machine Learning
2018-01-01-2020-01-01
NUST School of Electrical Engineering and Computer Science
Bachelor of Engineering (BEng) in Electrical - Digital Systems and Signal Processing
2011-01-01-2015-12-01
Languages
- English
- Urdu
Github