Embedded software engineer with 3 years of experience in C, C++, and Python, primarily focused on device drivers and firmware development. I am currently open to new oppurtunities where I can contribute and grow.
My name is Ali Hassan Shah, and I have experience working in international companies such as AMD and Espressif Systems, where I contributed to both system-level and customer-facing software projects.
At AMD (Shanghai, China), I initially joined the GPU software team as a developer but began by working as a software tester to gain deep familiarity with the kernel driver stack. This gave me valuable insight into the system architecture and led me to take the initiative to develop a unified RAS tool capable of supporting multiple ASIC generations. In addition, I’ve contributed to the development of internal debugging tools leveraging modern LLM workflows, making issue triage and system bring-up more efficient. My primary coding work involves C, C++, and Python, with a focus on reliability and scalability.
At Espressif Systems (Shanghai, China), I worked on customer-facing IoT projects using the ESP32 platform, delivering complete solutions with standardized software stacks in C and C++. These projects included Matter-compliant devices and edge AI use cases, where I collaborated closely with customers to ensure robust integration.
I’m passionate about embedded software design, particularly for tiny, resource-constrained devices, and enjoy bridging the gap between low-level hardware and high-level system software.
Python, C, C++, Shell scripting
Device drivers, I2C/SPI/UART protocols, RTOS
Pytest, Test planning, Regression testing, Integration testing
Azure DevOps, Jenkins, GitLab CI/CD, Docker
Git, GitHub, GitLab
Jira, Agile, Confluence
Linux (Ubuntu, RHEL)
FUDAN University, Shanghai, China
Specialized in Electronics Engineering with focus on embedded systems, high speed electronics, HDL Based design and advanced digital systems.
COMSATS University of Science and Technology, Abbottabad, Pakistan
Specialized in Electronics Engineering with focus on embedded systems, electrical engineering, and digital systems.
AI Adoption for Kernel Log Analysis (August 2024 – Present)
• Proposed and secured leadership buy-in for an AI-driven tool to assist kernel developers in triaging errors
• Led cross-team collaboration (IQE, CVS, and Linux kernel teams) to integrate LLM-based analysis of kernel logs, reducing manual effort and improving issue triaging efficiency
• Designed and implemented a Go-based framework to parse and categorize kernel logs before passing them to the LLM for actionable insights
Unified RAS Tool Development (January 2024 – July 2024)
• Contributed to unifying the RAS (Reliability, Availability, Serviceability) Tool as a Linux userspace toolkit across multiple ASICs
• Worked closely with senior developers to add new hardware support and refactor existing codebase for maintainability and scalability
RAS Tool for Virtualization (September 2024 – December 2024)
• Enhanced the RAS Tool to simulate hardware error cases, validating driver compatibility under virtualization environments
• Developed features supporting SR-IOV use cases with multiple VMs on server-class infrastructure
IQE Testing Framework for Data Center GPUs (February 2024 – August 2024)
• Gained hands-on expertise in the Linux kernel development and integration process
• Performed regression and integration testing of kernel patches and firmware
• Built a Bash-based CI automation framework for test execution, later leading its migration to a Pytest framework for better maintainability and extensibility
• Partnered with developers and QA teams to accelerate debugging and improve release quality
Key Responsibilities
• Designed and maintained production-ready embedded software for ESP32-based IoT devices using C/C++
• Integrated device drivers (I2C/SPI/UART) and implemented Matter protocol support for smart home applications
• Developed customer-facing solutions and work with them to provide them end to end support
Key Technical Projects
ESP32 Smart Knob Controller (Aug 2023 – Dec 2023)
• Architected Matter-compliant smart home rotary controller with sub-millisecond response times
• Developed advanced encoder driver with hardware interrupt-driven quadrature decoding
• Implemented complete Matter protocol stack with Thread/Wi-Fi networking and device commissioning
• Designed LVGL-based GUI for real-time monitoring and control interface
ESP-FOC Driver Development (Aug 2023 – Dec 2023)
• Engineered Field-Oriented Control driver for ESP32 achieving 95%+ motor efficiency
• Implemented Clarke/Park transformations and Space Vector Modulation algorithms
• Developed hardware abstraction layer supporting 8+ motor driver chipsets (DRV8302, DRV8313)
• Created real-time control framework with 20kHz deterministic control loops
ESP-BOX AI Assistant (May 2023 – July 2023)
• Integrated ESP-BOX with OpenAI APIs, developing C library and LVGL-based GUI chatbot for edge AI
• Reduced latency issues on constrained devices through optimized implementation
• Enabled TinyUF2 bootloader support for secure and simplified firmware deployment
Matter Button Driver (Jan 2023 – Apr 2023)
• Developed Matter-compliant button driver enabling single button to perform multiple tasks without breaking APIs
• Created Arduino wrapper for easy customer adoption of Matter-compliant devices
• Provided end-to-end support for Matter certification including software alignment and UL lab testing
ESP Edge AI Framework (Sep 2022 – Jan 2023)
• Delivered customer-facing Edge AI solutions on ESP32-S3, strengthening real-world application adoption
• Deployed CNN-based hand gesture recognition achieving ~0.7s on-device inference without cloud dependency
• Built human activity recognition model using accelerometer data + ESP-DL
• Developed Python Tkinter GUI for training & deploying custom AI models, simplifying customer onboarding
Interior Car Lamp Feedback System
• Designed feedback control system with Melexis 81118 IC using C programming for real-time color temperature correction
• Implemented PWM-based voltage adjustment achieving precise automotive lighting control
• Conducted research on circadian light effects with experimental design and data analysis using Python and SPSS
• Developed embedded control algorithms for adaptive automotive lighting systems
• Set up WCDMA and LTE testing environments using Linux and Shell scripting
• Configured Radio Base Stations (RBS) and executed verification tests for telecom features
• Gained experience with Git/Gerrit, JIRA, and Agile methodologies
Espressif Projects
Matter-compliant smart home rotary controller with precision encoder processing and advanced firmware development
Espressif Projects
Advanced Field-Oriented Control driver for ESP32 with 95%+ efficiency and real-time performance optimization
Espressif Projects
Edge AI chatbot with OpenAI integration, featuring optimized C library implementation and LVGL-based GUI for constrained devices
Espressif Projects
Production-ready Matter-compliant button driver with multi-function operation, Arduino compatibility, and full UL certification support
Espressif Projects
CNN-based hand gesture recognition achieving ~0.7s on-device inference with Python training tools and comprehensive customer deployment framework
Espressif Projects
Advanced analog input processing with interactive simulation, event-driven architecture, and comprehensive hardware integration for embedded control systems
Other Projects
Real-time color temperature correction for automotive lighting using Melexis IC and PWM control
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