Embedded Systems Engineer In Ai Resume Example
Professional ATS-optimized resume template for Embedded Systems Engineer In Ai positions
John Doe
Embedded Systems Engineer in AI
Email: john.doe@email.com | Phone: (555) 123-4567 | LinkedIn: linkedin.com/in/johndoe | GitHub: github.com/johndoe
PROFESSIONAL SUMMARY
Innovative Embedded Systems Engineer specializing in AI-driven hardware solutions with over 6 years of experience integrating machine learning algorithms into real-time embedded platforms. Adept at designing scalable firmware, optimizing low-power embedded architectures, and collaborating cross-functionally to develop intelligent IoT devices. Passionate about leveraging edge AI to enhance device autonomy and responsiveness in dynamic environments.
SKILLS
Hard Skills
- Embedded C/C++, C#, Python
- Real-Time Operating Systems (RTOS): FreeRTOS, Zephyr
- FPGA & SoC development (Xilinx, Intel FPGA)
- Machine Learning frameworks: TensorFlow Lite, Edge Impulse, TVM
- Hardware design: PCB design, schematic capture (Altium Designer, KiCad)
- Protocols: I2C, SPI, UART, CAN, Ethernet
- Wireless tech: Bluetooth LE, Wi-Fi, Zigbee
- Optimization for low-power and resource-constrained environments
- Cloud integration: AWS IoT, Azure IoT Edge
Soft Skills
- Problem-solving with a systems-thinking approach
- Cross-team communication and technical mentoring
- Agile development and continuous integration
- Technical documentation and knowledge sharing
- Customer-focused innovation mindset
WORK EXPERIENCE
*Senior Embedded Systems Engineer – AI Solutions*
*InnovateAI Technologies, San Francisco, CA*
June 2022 – Present
- Led development of edge AI modules integrated into autonomous drones, reducing latency by 30% via optimized firmware and hardware acceleration.
- Designed and deployed custom TinyML models for real-time object detection on resource-limited microcontrollers, achieving 85% accuracy.
- Collaborated with data scientists to adapt neural network models for embedded deployment, ensuring compatibility with constrained hardware environments.
- Mentored junior engineers on low-level embedded programming and efficient AI inference techniques.
*Embedded Firmware Engineer – IoT & AI Devices*
*NextGen Embedded, Austin, TX*
July 2018 – May 2022
- Developed firmware for smart wearable devices featuring embedded machine learning for activity recognition, increasing device battery life by 20%.
- Implemented secure OTA (over-the-air) firmware updates and integrated edge AI for on-device sensor data processing.
- Collaborated with hardware teams to optimize sensor interfacing and power management, resulting in improved device stability in field deployments.
- Contributed to open-source firmware libraries used across multiple product lines, enhancing reusability.
EDUCATION
**Bachelor of Science in Electrical Engineering**
University of Texas at Austin, TX
*2014 – 2018*
CERTIFICATIONS
- **Certified IoT Professional (CIoTP)** – IoT Association, 2023
- **Deep Learning Specialization** – Coursera (DeepLearning.AI), 2022
- **Embedded Systems Programming Certificate** – ARM Accredited Training, 2021
PROJECTS
- **Autonomous Agricultural Drone**
Developed an embedded AI system enabling crops monitoring through real-time image recognition, increasing yield predictions accuracy by 40%.
- **Edge AI Smart Security Camera**
Created a compact, battery-powered security camera with embedded facial recognition, capable of operating seamlessly for 72 hours without cloud connectivity.
TOOLS & TECHNOLOGIES
- Embedded Development: ARM Cortex-M, RISC-V, FPGA, MicroPython
- AI Frameworks: TensorFlow Lite for Microcontrollers, Edge Impulse, TVM
- Hardware Design: Altium Designer, KiCad
- Cloud Platforms: AWS IoT Core, Azure IoT Edge
- Version Control: Git, Jenkins, GitLab CI/CD
LANGUAGES
- English (Native)
- Spanish (Fluent)
*References available upon request.*
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