Principal Engineer AI - App Deployment (On-site in Abu Dhabi)

Principal Engineer AI - App Deployment

Job Description

Be part of a global leader in advanced defense and technology solutions, driving innovation across aerospace, cyber defense, autonomous systems, and precision-guided munitions. Our organization supports national security and global stability through cutting-edge research, engineering excellence, and a commitment to mission success

The TinyML / Embedded AI Principal Engineer is responsible for designing, developing, optimizing, and deploying AI solutions on resource-constrained embedded devices for advanced programs.
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.

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.
• 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.
• Mentoring and training of junior technical personnel.

Requirements:

Accept responsibility for technical performance at subsystem and full product level.
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.
Understanding of systems engineering processes (acceptance, qualification, and field testing)
Experience with edge computing constraints: low power, limited compute, limited bandwidth.
Hardware-software integration for AI workloads.
Accept responsibility for technical performance at subsystem and full product level.
Communication with technical and non-technical personnel, as well as service providers.

Qualifications:

Preferable: PhD or 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
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.