Artificial Intelligence (AI) Engineer

Artificial Intelligence (AI) Engineer Location: Penang, Malaysia We are looking for a passionate and skilled Artificial Intelligence (AI) Engineer who is eager to work on cutting-edge AI technologies, including convolutional neural networks (CNNs) and generative AI (GenAI). This role offers the opportunity to work at the forefront of deep learning innovation—training and optimizing advanced architectures such as CNNs ,transformers, and diffusion models, while applying best practices in model efficiency and scalability. You’ll be part of a team leading the full AI model development cycle, from innovative research and architecture design to optimizing and deploying models that make a tangible impact in real-world applications. Responsibilities: • Design, train, and evaluate advanced machine learning models, focusing on CNNs and GenAI (e.g., transformers, diffusion models). • Optimize models for performance, scalability, and resource efficiency using techniques like pruning, quantization, and knowledge distillation. • Conduct experiments, benchmark model performance, and iterate rapidly to improve outcomes. • Collaborate with cross-functional teams to deploy models into production environments. • Explore new architectures and training techniques, contributing to the team’s ongoing research and innovation. • Stay current with emerging trends in AI and deep learning, and share insights with the team. Qualifications: • Ph.D. or Master's in Computer Science, Artificial Intelligence, Electrical Engineering, or a related field. • Strong experience developing deep learning models with PyTorch or TensorFlow. • Solid understanding of model optimization and compression techniques. • Proficiency in Python, with additional experience in C/C++ a plus. • Track record of research, publications, or practical projects in deep learning. • Deep expertise in CNN architecture design and optimization. • Experience working on generative models such as transformers, VAEs, or diffusion models. • Experience deploying models in production environments or integrating with larger systems.