Software Engineer (AI/ML)

Salary: RM 3,800 – RM 5,500 per month

Key Responsibilities

• Design, develop, train, evaluate, and deploy Machine Learning and Deep Learning models using Python.

• Develop AI solutions for object detection, image classification, image segmentation, anomaly detection, and industrial inspection.

• Implement and optimize deep learning architectures using TensorFlow, PyTorch, or Keras.

• Apply transfer learning, fine-tuning, and data augmentation techniques to improve model accuracy and robustness.

• Design and implement advanced neural network architectures including CNNs, Vision Transformers (ViTs), RNNs, Autoencoders, GANs, and Transformer-based models.

• Build scalable training pipelines supporting single-GPU, multi-GPU, and distributed training (DDP).

• Optimize AI models for high-performance inference using CUDA, TensorRT, ONNX, and cuDNN.

• Perform image preprocessing and enhancement using OpenCV or similar image processing libraries.

• Prepare, clean, annotate, and manage datasets for model training and validation.

• Evaluate model performance using appropriate metrics and continuously improve model accuracy and inference speed.

• Collaborate with software engineers to integrate AI models into desktop, embedded, or cloud-based applications.

• Participate in code reviews, technical discussions, and maintain technical documentation.
  

Required Skills

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Electronics, or a related field.

• Minimum 1 years of hands-on experience in Machine Learning, Deep Learning, or Computer Vision.

• Strong programming skills in Python.

• Strong understanding of Machine Learning and Deep Learning concepts.

• Hands-on experience with PyTorch, TensorFlow, or Keras.

• Strong knowledge of Computer Vision using OpenCV.

• Experience developing one or more of the following:

  • Object Detection (YOLO, Faster R-CNN, SSD)

  • Image Segmentation (U-Net, Mask R-CNN, YOLO Segmentation)

  • Image Classification

  • Anomaly Detection (PatchCore, Autoencoders, EfficientAD, or similar)

• Good understanding of image processing techniques such as filtering, thresholding, morphology, feature extraction, and image enhancement.

• Experience with GPU computing and model optimization using CUDA-enabled environments.

• Familiarity with Linux and Windows development environments.

• Strong analytical, debugging, and problem-solving skills.

• Good communication and teamwork skills.

Preferred Skills (Good to Have)

• Experience with NVIDIA TensorRT, ONNX Runtime, CUDA, and cuDNN.

• Experience with distributed training using DDP or multi-GPU environments.

• Familiarity with Docker and containerized AI deployments.

• Experience with NVIDIA Jetson platforms or edge AI deployment.

• Familiarity with Git and version control systems.

• Exposure to HALCON or other industrial machine vision libraries.

• Experience integrating AI models into production software applications.

• Experience working on manufacturing, industrial automation, quality inspection, or machine vision projects.

• Basic knowledge of cloud platforms such as AWS, Azure, or Google Cloud is an added advantage.

What We Offer

• Opportunity to work on cutting-edge AI, Machine Learning, and Computer Vision technologies.

• Exposure to industrial automation, machine vision, and smart manufacturing solutions.

• Hands-on experience with high-performance GPU computing and AI optimization.

• Opportunity to work with the latest Deep Learning and Generative AI models.

• Collaborative engineering environment with continuous learning and career growth.

• Opportunity to develop production-ready AI solutions deployed in real-world manufacturing environments.

Nice to Have

Candidates with experience in any of the following will have an added advantage:

• Computer Vision

• Machine Vision

• Industrial Automation

• Deep Learning

• YOLO

• OCR

• Image Segmentation

• Anomaly Detection

• PatchCore

• EfficientAD

• OpenCV

• HALCON

• TensorFlow

• PyTorch

• CUDA

• TensorRT

• ONNX Runtime

• NVIDIA Jetson

• Multi-GPU Training (DDP)

• Docker

• Git

• Edge AI