Note: The job is a remote job and is open to candidates in USA. OVA.Work is seeking an Embedded AI Engineer to design, develop, and deploy AI-powered applications on embedded systems. The role involves integrating AI models into embedded software and collaborating with cross-functional teams to create intelligent solutions for various industries.
Responsibilities
- Design, develop, and deploy AI/ML applications on embedded devices and microcontrollers
- Integrate machine learning and deep learning models into embedded software and firmware
- Optimize AI models for low-power, low-memory, and real-time inference using quantization, pruning, and compression techniques
- Develop embedded software using C/C++, Python, and embedded programming frameworks
- Deploy AI models using TensorFlow Lite, TensorFlow Lite Micro, ONNX Runtime, TensorRT, OpenVINO, or similar inference frameworks
- Interface AI applications with sensors, cameras, microphones, actuators, and communication modules
- Collaborate with hardware, firmware, AI, and software engineering teams to build end-to-end intelligent embedded systems
- Develop and optimize drivers, middleware, and application software for AI-enabled devices
- Benchmark system performance, memory usage, latency, and power consumption
- Implement secure boot, firmware updates, and device security best practices
- Perform debugging, testing, validation, and troubleshooting across hardware and software components
- Document system architecture, software design, deployment procedures, and technical specifications
- Stay current with advancements in embedded AI, TinyML, AI accelerators, and edge computing technologies
Skills
- Bachelor's or Master's degree in Computer Science, Electronics, Embedded Systems, Electrical Engineering, Artificial Intelligence, Robotics, or a related field
- 3–8+ years of experience in embedded systems, firmware development, AI/ML, or related software engineering roles
- Strong programming skills in C/C++ and Python
- Experience developing software for embedded Linux or RTOS environments
- Hands-on experience with machine learning and deep learning model deployment
- Knowledge of hardware interfaces such as UART, SPI, I2C, CAN, GPIO, USB, and Ethernet
- Experience working with ARM-based processors, microcontrollers, or embedded platforms
- Understanding of software optimization, debugging, and performance profiling techniques
- Experience with NVIDIA Jetson, Raspberry Pi, STM32, ESP32, NXP, Qualcomm, Texas Instruments, Renesas, or similar embedded platforms
- Knowledge of TinyML and AI deployment on microcontrollers
- Experience with computer vision, speech recognition, sensor fusion, or robotics applications
- Familiarity with FPGA or AI accelerator hardware is an advantage
- Experience with OTA firmware updates and device fleet management
- Relevant certifications in embedded systems, AI, cloud, or IoT technologies
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