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.________________________________________ 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, TensorRTo Qualcomm QCS/QCM chips + SNPE SDKo Intel Movidius / OpenVINO + RealSenseo 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.________________________________________ 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.________________________________________ 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. (1.) Key Responsibilities
1. Establish and manage the technology solutions organization to achieve growth targets for designated accounts.
2. Develop and execute strategic visions and roadmaps for solution development.
3. Implement skill development plans for the LoB/Technology Solutions team.
4. Establish and maintain thought leadership in technology and domain practices.
5. Provide leadership and direction to solution development teams.
6. Define, establish, and manage a re-use practice across the LoB.
7. Engage with clients and stakeholders to ensure high-quality solution delivery.
8. Monitor and report on the performance of the technology solutions organization.