Artificial Intelligence Engineer

• Deep Learning Frameworks: PyTorch
• Programming: Python (Advanced), OOP (Object-Oriented Programming).
• Computer Vision Tasks: Object Detection, Semantic Segmentation, Instance Segmentation, Multi-Object Tracking (MOT), Lane Detection.
• Libraries: OpenCV, NumPy, Scikit-learn, TorchVision.
• Model Optimization: TensorRT, ONNX, Quantization (INT8/FP16), Pruning, Model Compression.
• Architectures: Transformers (ViT), ResNet, YOLO (v8/v10), Mask R-CNN, DeepSORT.
• Infrastructure: Docker, CUDA, Git, MLOps.

Automotive & BEV Specialized Terms

• BEV (Birds Eye View): Perspective Transformation, Spatial-Temporal Transformers, Lift-Splat-Shoot (LSS).
• Autonomous Driving (AD): ADAS (Advanced Driver Assistance Systems), Perception Pipeline, Sensor Fusion.
• Sensor Modalities: LiDAR, Radar, Camera-only (Vision-centric), Ultrasonic.
• Data Formats: Point Clouds, NuScenes Dataset, KITTI Dataset, Waymo Open Dataset.