Senior AI Engineer

Responsibilities & Context

• Design, develop, and optimize AI/ML models for mobile and wearable devices.
• Deploy AI models on smart devices using frameworks such as TensorFlow Lite, Core ML, ONNX Runtime, or Qualcomm SNPE.
• Optimize AI models for performance and power efficiency on resource-constrained devices.
• Implement real-time inference on embedded platforms while considering latency, battery consumption, and hardware constraints.
• Work with hardware accelerators like GPU and NPU (e.g. Tensor Processing Unit, Qualcomm AI Engine).
• Preprocess and analyze data for AI applications in mobile and wearable environments.
• Collaborate with cross-functional teams including firmware engineers, mobile developers, and product managers.
• Stay updated with the latest advancements in edge AI, federated learning, and efficient deep learning architectures.

Requirements

Education

• Bachelor of Science (BSc) in Computer Science & Engineering
• Bachelor of Science (BSc) in Electrical & Electronic Engineering
• Bachelor of Science (BSc) in Information Technology

Experience

• At least 2 years
• The applicants should have experience in the following business area(s): Research Organization, IT Enabled Service, Software Company

Additional Requirements

• Proficiency in Python and experience with AI frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Experience with mobile AI deployment using TensorFlow Lite, Core ML, or ONNX Runtime.
• Knowledge of optimizing deep learning models using quantization, pruning, and model compression techniques.
• Hands-on experience deploying AI models on Android (via ML Kit, NNAPI) or iOS (via Core ML, Metal).
• Strong understanding of edge computing, power-efficient AI, and real-time inference.
• Familiarity with cloud-edge integration for AI workloads (e.g., cloud-assisted AI inference).
• Knowledge of embedded AI development with tools like Edge TPU, OpenVINO, or TinyML is a plus.