Responsibilities: * Install CCTV systems,Dash camers and WiFi devices * Test AirFi smart infotainment devices on-site * Maintain customer satisfaction through timely resolutions * Meet bus operators and maintain a rapo * Annual bonus
About the Role: We are seeking a passionate and skilled AI/ML Engineer to join our team and lead the development of a smart passenger counting system for public transport using camera-based detection, face recognition, and re-identification (Re-ID) techniques. You will design and deploy models that detect, track, and identify passengers to provide accurate in/out counts while preventing duplicate recognition. Key Responsibilities: Develop computer vision models for real-time passenger detection and tracking on moving buses. Implement face detection, facial feature extraction, and re-identification algorithms to count passengers without double-counting. Optimize models for edge devices (e.g., Jetson Nano, Raspberry Pi + Coral TPU, etc.). Train and fine-tune custom datasets for face recognition and Re-ID under varying lighting and motion conditions. Work with hardware and software teams to integrate your solutions into an onboard system (camera, processor, storage). Build privacy-compliant data pipelines and anonymization logic for onboard or cloud processing. Benchmark and evaluate model performance (FPS, accuracy, latency, power usage). Collaborate with frontend/backend engineers to feed analytics into dashboards or APIs. Must-Have Skills: Strong background in Computer Vision, Deep Learning, and Machine Learning. Proficiency in Python and deep learning frameworks like TensorFlow, PyTorch, or ONNX. Experience with OpenCV, face detection libraries (e.g., MTCNN, Dlib, FaceNet), and object tracking (SORT/Deep SORT/ByteTrack). Knowledge of face re-identification and metric learning (Siamese/Triplet networks). Experience deploying models on edge devices (Jetson, Pi, Coral TPU, etc.). Understanding of data privacy and GDPR compliance in facial recognition systems. Nice to Have: Experience with YOLO, ByteTrack, MMDetection, DeepStream SDK, or GStreamer. Hands-on with video analytics, RTSP streaming, and camera sensor calibration. Knowledge of hardware interfacing, embedded Linux, or IoT deployments. Familiarity with passenger flow analytics, transportation systems, or smart surveillance solutions. Qualifications: Bachelors or Masters degree in Computer Science, AI/ML, Data Science, or related field. 2+ years of hands-on experience in building and deploying AI-powered vision systems. Strong analytical, debugging, and communication skills. What We Offer: Opportunity to work on cutting-edge smart transportation solutions. Competitive salary and flexible working hours. Cross-functional collaboration in a growing tech-driven environment. Freedom to innovate and bring your ideas into production.