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Niche embedded AI skillset reduces applicant pool despite metro Mumbai location.
Highly specialized embedded edge AI and automotive perception work limits cross-industry transferability.
Multiple mandatory tools and embedded AI experience enforce strict technical screening.
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Integrate AI perception models and optimize real-time edge unified road perception systems on automotive-grade embedded hardware.
Develop validation, testing, and benchmarking pipelines for computer vision tasks including object detection, segmentation, lane detection, and dynamic object tracking.
Evaluate and optimize system performance metrics like latency, FPS, accuracy, memory usage, and GPU/CPU utilization across platforms including NVIDIA Jetson, Qualcomm Snapdragon, and ARM devices.
Degree in Computer Science, Software Engineering, Electrical Engineering, or related field with relevant industry experience.
Strong programming skills in C++ and Python.
Experience with Linux-based embedded systems and real-time software environments.
Hands-on experience deploying AI models in production using tools such as CUDA, TensorRT, ONNX Runtime, and OpenCV.
Experience in performance profiling and optimization of edge AI systems, including automated testing and benchmarking.
Familiarity with computer vision pipelines and evaluation metrics related to object detection and tracking.
Background in automotive perception, ADAS systems, or embedded SoC platforms is a plus but not mandatory.