As a Python / Computer Vision Developer, you will be responsible for the design, development, and implementation of innovative computer vision and AI-based solutions in the logistics and automation domain. Your primary role will include developing and optimizing algorithms for object detection, recognition, tracking, and OCR/ANPR using Python and modern deep learning frameworks.
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Responsibilities:
- Designing, coding, testing, debugging, and documenting programs.
- Supporting activities aligned with corporate AI/ML and systems architecture.
- Ensuring support in analysis, model design, and system integration during all phases of solution delivery.
- Recommending the most effective approaches for deployment and scalability.
- Working on new research concepts, training pipelines, and AI model deployment across real-time video streams and edge/cloud environments.
Qualifications:
Typically requires 5–7 years of applicable experience in Python, machine learning, and computer vision, with hands-on exposure to frameworks such as OpenCV, PyTorch, TensorFlow, or PaddleOCR/YOLO.
Skill Requirements:
- Strong proficiency in Python (3.x) with a focus on AI/ML and computer vision development.
- Solid experience with OpenCV and at least one major deep learning framework (PyTorch, TensorFlow, Keras, or PaddlePaddle).
- Hands-on experience with object detection models (YOLO, Faster R-CNN, SSD) and OCR frameworks (e.g., PaddleOCR, Tesseract).
- Experience with real-time video/image processing (RTSP streams, edge devices, GPU acceleration).
- Good knowledge of data preprocessing, augmentation, and annotation tools (LabelImg, PPOCRLabel, CVAT).
- Familiarity with containerization (Docker), APIs, and cloud platforms (Azure, AWS, GCP) for model deployment.
- Understanding of SQL/NoSQL databases for storing and retrieving processed data.
- Knowledge of Git, CI/CD pipelines, and Agile methodologies.
Skill – Nice to Have:
- Experience with distributed systems and message queues (RabbitMQ, Kafka, gRPC).
- Familiarity with Edge AI deployment (NVIDIA Jetson, Intel OpenVINO, TensorRT).
- Background in mathematics/statistics for AI optimization.
- Exposure to C++ or .NET interop for hybrid solutions.
- Experience in the logistics, transportation, or industrial automation domain.
