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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