Embedded AI Engineer
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