Artificial Intelligence / Computer Vision Engineer

Requirements

• Bachelor's or master's degree in computer science, data science, artificial intelligence or related engineering fields.
• Minimum 3 to 5 years of proven experience in developing ML/DL architectures to solve real-world computer vision problems
• Strong proficiency in Python with hands-on experience in mainstream deep learning frameworks (PyTorch and TensorFlow) and their associated computer vision libraries (e.g., OpenCV, torchvision, timm, Hugging Face Transformers)
• In-depth understanding of digital image processing, image transformation techniques, real-time image analytics and feature extraction in computer vision
• Experience in large-scale image data processing and architecting of data pipelines
• Possess data storytelling, information visualisation and technical writing skills
• Excellent research and problem-solving abilities, whilst maintaining curiosity about new things and enthusiasm to learn and share latest methods/techniques within the team
• Hands-on experience with cloud platforms (AWS, GCP) and modern backend/serving stacks (FastAPI, Redis, Docker) for end-to-end AI model development, deployment, and scalable inference
• Command good communication and collaboration skills, with a strong sense of responsibility, integrity and reliability

Additional Information

Job responsibilities: Develop machine learning computer vision applications in object detection, classification, segmentation and recognition for gemstones Develop LLM-applications specific to gemstone domain leveraging on latest frameworks Conduct User requirements, scope the AI/computer-vision project and prepare documents to define nature of dataset required for machine-learning model training Work collaboratively with an entrepreneurial team of IOT-product engineers, machine learning & data scientist and software engineers, both in-house or Vendors, to successfully develop and deploy a cutting-edge solution Develop a comprehensive MLOps environment to manage the entire lifecycle and pipeline of machine learning projects, as well as maintain a well-documented code repository Conduct research and product development projects using internal gatekeeping process, prepare project and user requirements specifications, execute experiments and investigations, document and report findings, communicate to key stakeholders, and handover to downstream teams Execute the necessary data preparation & pre-processing, AI-model building, testing, validation and deployment to ensure reliable and scalable AI solution

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