Edge AI Principal

Overview :

As an IoT and AI Architect for Edge Computing, you will be responsible for designing, developing, and deploying advanced IoT solutions with AI capabilities for edge devices. You will work on integrating AI-driven solutions directly into the edge devices to enhance real-time data processing, reduce latency, and enable efficient decision-making. You will collaborate with cross-functional teams to build scalable and resilient architectures that can handle large volumes of data generated at the edge.

Key Responsibilities

• Architect large-scale, distributed Edge AI systems for real-time inference across thousands of devices.
• Lead hardware-aware model development optimized for deployment on platforms like NVIDIA Jetson, Qualcomm Snapdragon, Intel Movidius, and ARM Cortex-A / NPU chips.
• Design pipelines for edge inferencing with low-latency, low-power, and high-accuracy constraints.
• Lead performance tuning using tools like TensorRT, ONNX Runtime, TVM, and OpenVINO.
• Build scalable, secure model deployment platforms using NVIDIA Triton, Edge Kubernetes (K3s), and MLOps frameworks.
• Integrate AI with real-world data sources : video streams, sensor fusion (IMU, LiDAR), audio, etc.
• Collaborate with firmware, hardware, and platform teams for system-level optimization.
• Define strategies for OTA model updates, fleet-wide monitoring, and privacy-preserving AI.
• Stay ahead of emerging trends in tinyML, neural architecture search (NAS), zero-shot inference, and federated learning.

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️ Required Technical Skills

o NVIDIA Jetson (Orin, Xavier, Nano) + DeepStream, TensorRT

o Qualcomm QCS / QCM chips + SNPE SDK

o Intel Movidius / OpenVINO + RealSense

o ARM NN / Ethos-U / TFLite Micro

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Soft Skills & Leadership

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Preferred Qualifications

This role requires a blend of expertise in IoT systems, edge computing, AI, and cloud technologies, making it a critical position for organizations looking to implement smart, real-time, and scalable solutions at the edge.