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
• Deep expertise in Edge AI systems, embedded deep learning, and AI model compression.
• Strong knowledge of:
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
• Model optimization: quantization (INT8/FP16), pruning, distillation, custom kernels.
• Frameworks: PyTorch, TensorFlow Lite, ONNX, TVM, Apache MXNet (optional).
• Experience with edge deployment stacks: Docker, Kubernetes (K3s, MicroK8s), Yocto, RTOS.
• Proficiency in Python, C++, and low-level embedded programming.
• Strong understanding of network protocols, remote diagnostics, OTA updates, and device provisioning.
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Soft Skills & Leadership
• 10+ years of experience in AI/ML, with at least 3–5 years in edge/embedded environments.
• Proven experience architecting and shipping commercial Edge AI products.
• Strong cross-functional leadership with ability to bridge hardware, software, and product teams.
• Ability to guide engineering trade-offs (latency, accuracy, memory, power) under real-world constraints.
• Effective communicator with stakeholders at all levels—technical and non-technical.
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Preferred Qualifications
• MS or PhD in Electrical Engineering, Computer Science, Robotics, or related field.
• Experience with robotics (ROS/ROS2), autonomous systems, or industrial IoT deployments.
• Familiarity with synthetic data generation and AI simulation environments (NVIDIA Omniverse, Unity).
• Experience deploying multi-modal AI (vision, speech, sensor fusion) at the edge.
• Background in cybersecurity for edge devices and AI on air-gapped networks.
• Patents or publications in edge AI, embedded ML, or real-time AI inference.
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.

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