Principal Engineer - TinyML / Embedded AI
- Abu Dhabi
- Permanent
- Full-time
This position requires deep expertise in both hardware and software for embedded AI, covering the full lifecycle from model design and quantization to hardware integration, testing, and field deployment.
The incumbent will lead technical initiatives in real-time AI inference, sensor fusion, and on-device intelligence, enabling mission-critical performance under strict latency, power, and memory constraints.
The role also drives capability growth for embedded AI across the organization.Key Responsibilities
- Design, implement, and deploy TinyML / Embedded AI models for real-time inference on microcontrollers, SoCs, FPGAs, and custom accelerators.
- Apply quantization, pruning, and compression techniques to optimize speed, power efficiency, and memory footprint for embedded AI models..
- Select and integrate embedded AI hardware accelerators (e.g., NVIDIA Jetson, ARM Ethos-U, Kendryte, FPGA, ASIC).
- Develop and deploy real-time CV and multi-sensor fusion algorithms for video, radar, LiDAR, IMU, and other modalities.
- Implement robust object detection, classification, and tracking under challenging conditions.
- Integrate AI solutions into embedded/mechatronic systems, ensuring reliability, scalability, and security.
- Lead hardware-in-the-loop (HIL) simulations, lab validation, and field qualification testing for embedded AI systems.
- Mentoring and training of junior technical personnel.
- Provide technical authority for embedded AI deployment, hardware-software co-design, and AI optimization strategy.
- Produce technical documentation, risk assessments, and progress reports for internal and external stakeholders..
- Research and adopt emerging TinyML frameworks (e.g., TensorFlow Lite Micro, Edge Impulse, PyTorch Mobile) and edge AI toolchains.
- Collaborate with cross-functional teams to design AI-enabled embedded architectures for next-gen systems.
- Generation of risk/progress reports, presentations and initiate proposals of mitigation action.
- 15+ years in developing and deploying AI solutions for embedded or defense systems.
- Proven record in edge AI optimization (quantization, pruning, compression).
- Hands-on experience with AI hardware toolchains (TensorRT, ARM CMSIS-NN, OpenVINO, Vitis AI).
- Exposure to NLP, robotics, predictive analytics, and autonomous systems is an advantage.
- Advanced experience with programming and software engineering, AI tools, frameworks and methodology.
- Advanced experience with edge computing and computational power optimization.
- Master's in Computer Science, Computer Engineering, Electrical Engineering, or related field.
- AI-related certifications in TinyML, embedded systems, or hardware acceleration are a plus.
- Training/qualifications in AI-related technologies will be an advantage.
- Expert-level skills in ML/DL, computer vision, and sensor fusion for embedded devices.
- Thorough understanding of the systems engineering process. (Acceptance testing, qualification, field testing, flight testing.)
- Integration, testing, and evaluation of AI systems in real-world applications.
- Experience with edge computing constraints: low power, limited compute, limited bandwidth.
- Hardware-software integration for AI workloads.