Embedded AI Engineer

Embedded AI Engineer - Job Description

Job 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