TinyML / Embedded AI Principal Engineer

Position Title:

Location:

Experience Required:

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Role Purpose:

The TinyML / Embedded AI Principal Engineer will lead the design, development, optimization, and deployment of AI solutions on edge and resource-constrained embedded devices. This role is critical to enabling real-time, mission-critical AI inference across advanced programs, with a focus on sensor fusion, hardware-software co-design, and embedded intelligence under strict latency, power, and memory constraints.

Key Responsibilities:

• Architect and deploy TinyML / Embedded AI models for real-time inference on microcontrollers, SoCs, FPGAs, and custom accelerators.
• Optimize AI models using quantization, pruning, and compression for performance and efficiency.
• Select and integrate embedded AI hardware platforms (e.g., NVIDIA Jetson, ARM Ethos-U, Kendryte, FPGA, ASIC).
• Develop real-time computer vision and multi-sensor fusion algorithms (video, radar, LiDAR, IMU).
• Implement robust object detection, classification, and tracking under challenging conditions.
• Integrate AI into embedded/mechatronic systems ensuring reliability, scalability, and security.
• Lead HIL simulations, lab validation, and field testing of embedded AI systems.
• Mentor junior engineers and drive capability growth across the organization.
• Serve as technical authority on embedded AI deployment and optimization strategies.
• Produce technical documentation, risk assessments, and stakeholder reports.
• Research and adopt emerging TinyML frameworks (TensorFlow Lite Micro, Edge Impulse, PyTorch Mobile).
• Collaborate with cross-functional teams to design next-gen AI-enabled embedded architectures.
• Generate risk/progress reports and propose mitigation strategies.

Experience & Skills:

• 15+ years of experience in AI development for embedded or defense systems.
• Proven expertise 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 a plus.
• Advanced programming and software engineering skills.
• Deep understanding of edge computing and computational power optimization.

Qualifications:

• Master's or PhD in Computer Science, Electrical Engineering, or related field.
• Certifications in TinyML, embedded systems, or hardware acceleration are a plus.
• Additional training in AI-related technologies is an advantage.