AI Engineer (Computer Vision & Deep Learning)

About the RoleWe are looking for an AI Engineer to build, train, and optimize computer vision systems for real-time highway traffic analysis. The core focus of this role is improving accuracy and reliability of image and video based detection systems, including ANPR (automatic number plate recognition) and traffic compliance classification (seatbelt detection, helmet detection, vehicle categorization, and similar use cases).

Key ResponsibilitiesDesign and scale pipelines to process continuous video feeds and high resolution images from highway camera infrastructureImprove ANPR reading accuracy across varying lighting, weather, angle, and speed conditionsTrain, fine tune, and optimize deep learning models for traffic compliance classification: seatbelt detection, helmet detection, multi class vehicle categorization, and related tasksContinuously benchmark and improve model accuracy through better data curation, augmentation, and retraining cyclesOptimize models for high throughput, low latency inference on edge computing hardwareCurate, label, and manage large scale image/video datasets for ongoing model improvementRequired Skills & ExperienceMinimum 3 years of hands on industry experience specifically in image processing and video processing (not just general ML)Proven experience improving accuracy of classification/detection models in production, not just training on benchmark datasetsStrong proficiency in PyTorch or TensorFlow, and OpenCVSolid experience with object detection (YOLO variants), image classification, and object trackingExperience working with real world outdoor/dynamic video data (traffic, surveillance, or similar domains preferred)Strong Python skills, production ready codeGood to HavePrior experience specifically with ANPR systemsExperience with RTSP/GStreamer or real time video streaming pipelinesModel optimization for edge deployment (TensorRT, ONNX, quantization)Experience with LLMs/VLMs for multimodal reasoning (not mandatory) About the RoleWe are looking for an AI Engineer to build, train, and optimize computer vision systems for real-time highway traffic analysis. The core focus of this role is improving accuracy and reliability of image and video based detection systems, including ANPR (automatic number plate recognition) and traffic compliance classification (seatbelt detection, helmet detection, vehicle categorization, and similar use cases).

Key ResponsibilitiesDesign and scale pipelines to process continuous video feeds and high resolution images from highway camera infrastructureImprove ANPR reading accuracy across varying lighting, weather, angle, and speed conditionsTrain, fine tune, and optimize deep learning models for traffic compliance classification: seatbelt detection, helmet detection, multi class vehicle categorization, and related tasksContinuously benchmark and improve model accuracy through better data curation, augmentation, and retraining cyclesOptimize models for high throughput, low latency inference on edge computing hardwareCurate, label, and manage large scale image/video datasets for ongoing model improvementRequired Skills & ExperienceMinimum 3 years of hands on industry experience specifically in image processing and video processing (not just general ML)Proven experience improving accuracy of classification/detection models in production, not just training on benchmark datasetsStrong proficiency in PyTorch or TensorFlow, and OpenCVSolid experience with object detection (YOLO variants), image classification, and object trackingExperience working with real world outdoor/dynamic video data (traffic, surveillance, or similar domains preferred)Strong Python skills, production ready codeGood to HavePrior experience specifically with ANPR systemsExperience with RTSP/GStreamer or real time video streaming pipelinesModel optimization for edge deployment (TensorRT, ONNX, quantization)Experience with LLMs/VLMs for multimodal reasoning (not mandatory)