Computer Vision Engineer

As a Python / Computer Vision Developer at the company, 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 involve developing and optimizing algorithms for object detection, recognition, tracking, and OCR/ANPR using Python and modern deep learning frameworks.

Responsibilities:

• Design, code, test, debug, and document programs.
• Support activities aligned with corporate AI/ML and systems architecture.
• Provide analysis, model design, and system integration throughout all phases of solution delivery.
• Recommend effective approaches for deployment and scalability.
• Work on new research concepts, training pipelines, and AI model deployment across real-time video streams and edge/cloud environments.

Qualifications:

• 5–7 years of applicable experience in Python, machine learning, and computer vision.
• 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 (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.

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 logistics, transportation, or the industrial automation domain.