Embedded AI Engineer - Job DescriptionJob Title Embedded AI Engineer
Location [City/Remote/Hybrid]
Employment Type Full-time / Contract
Job Summary We are seeking an Embedded AI Engineer to design, develop, and deploy AI-powered applications on embedded systems and resource-constrained devices. The ideal candidate will have expertise in embedded software development, machine learning, deep learning, and hardware acceleration to build intelligent, real-time solutions for industries such as automotive, consumer electronics, healthcare, robotics, industrial automation, and IoT.
Key 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.
Required Qualifications - 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.
Preferred Qualifications - 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.
Technical Skills - C/C++
- Python
- Embedded Linux
- RTOS (FreeRTOS, Zephyr, ThreadX)
- ARM Cortex Processors
- STM32
- ESP32
- TensorFlow Lite
- TensorFlow Lite Micro
- TensorFlow
- PyTorch
- ONNX Runtime
- TensorRT
- OpenVINO
- OpenCV
- CUDA
- TinyML
- Edge Impulse
- Computer Vision
- Deep Learning
- Machine Learning
- Model Quantization
- Model Pruning
- UART
- SPI
- I2C
- CAN
- GPIO
- MQTT
- Docker
- Git
- CI/CD
- REST APIs
Soft Skills - Analytical thinking
- Problem-solving
- Communication
- Collaboration
- Innovation
- Attention to detail
- Time management
- Adaptability
- Continuous learning
Key Deliverables - AI-enabled embedded software and firmware
- Optimized AI models for embedded deployment
- Real-time inference applications
- Hardware and software integration solutions
- Performance benchmarking and optimization reports
- Technical documentation
- Secure firmware deployment and update mechanisms
- System validation and testing reports
Success Metrics - AI model inference speed and accuracy
- Memory and power optimization
- System stability and reliability
- Successful deployment on target embedded hardware
- Reduction in latency and resource utilization
- Product quality and defect reduction
- Compliance with security, safety, and quality standards
- Timely delivery of embedded AI features and product releases
