Embedded System Engineer

Embedded AI Engineer – TinyML for Smart Agriculture Devices

Embedded AI | AgriTech | IoT

Are you ready to apply your AI and embedded systems skills to real-world impact?

Join our client's innovative agritech team developing ultra-low-power smart agriculture devices . As an Embedded AI Engineer , you’ll bring intelligence to the edge – enabling real-time crop, soil, and environmental monitoring with TinyML on microcontrollers .

Key Responsibilities

• Develop and deploy TinyML models for classification, anomaly detection, and predictive tasks on microcontrollers
• Optimize neural network inference using frameworks such as TensorFlow Lite for Microcontrollers or CMSIS-NN
• Interface with agricultural sensors (e.g., soil moisture, weather, multispectral) and integrate signal processing pipelines
• Ensure low-latency data processing within strict power and memory constraints
• Collaborate with agritech researchers and firmware engineers to iterate on data collection, model training, and performance tuning

Required Skills

• Experience with TinyML , including quantization, pruning, and deploying models to resource-constrained devices
• Familiarity with MCUs such as ARM Cortex-M (e.g., STM32, nRF52)
• Proficient in C/C++ and Python , with embedded systems and model prototyping experience
• Understanding of RTOS environments (e.g., FreeRTOS , Zephyr) and real-time signal acquisition
• Strong grasp of low-power design principles and memory optimization techniques

Bonus Points

• Prior experience in agricultural or environmental sensing systems
• Familiarity with edge computing protocols (e.g., LoRa, MQTT) for rural deployments
• Exposure to AutoML tools or federated learning at the edge