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