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Embedded AI Engineer at Festo SE & Co. KG

Bangalore, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

26 Sep, 26

Salary

0.0

Posted On

28 Jun, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, C/C++, Embedded Systems, Machine Learning, TensorFlow Lite, ONNX Runtime, Edge Impulse, Model Quantization, Model Pruning, ARM Cortex, STM32, ESP32, NVIDIA Jetson, Microcontroller Architectures, Real-time Embedded Systems, Hardware Acceleration

Industry

Automation Machinery Manufacturing

Description

Your job: • Develop, optimize, and deploy machine learning models for embedded and edge devices• Ensure real-time performance, memory efficiency, and low-power operation of on-device AI solutions• Integrate ML algorithms with microcontrollers, embedded controllers, and edge computing platforms• Implement pipelines for model conversion, quantization, and hardware acceleration• Collaborate with software, controls, and hardware engineers to deliver production-grade embedded AI systems• Participate in prototyping, testing, benchmarking, and continuous improvement of embedded AI solutions• Work with the global research teams to translate concepts into scalable prototypes for industrial automation Your qualification: Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Electronics, or related field 2–5 years of hands-on experience in Embedded Systems and AI/ML development Strong programming proficiency in Python, C/C++, and embedded development Practical experiencewith embedded ML frameworks such as TensorFlow Lite, ONNX Runtime, Edge Impulse, or similar Solid understanding of real-time embedded systems, microcontroller architectures, and communication protocols Experience with model optimization techniques (quantization, pruning, hardware-specific acceleration) is a plus Familiarity with edge hardware platforms such as ARM Cortex, STM32, ESP32, NVIDIA Jetson, or similar is desirable Ability to work in a fast-paced research-driven environment with strong ownership and autonomy

Responsibilities

Develop and deploy optimized machine learning models for embedded and edge devices to ensure real-time performance and low-power operation. Collaborate with cross-functional teams to translate research concepts into scalable prototypes for industrial automation.