Responsibilities

-Learning and tuning LLM and RAG models (fine-tuning, LoRA, etc.)

-Optimizing and deploying on-device and cloud environment models (ONNX, Qualcomm NPU, etc.)

-Building a RAG pipeline based on document embedding and vector retrieval

-Data preprocessing and pipeline automation

-Monitoring and improving model performance

Requirement:

-Candidate should possess a Bachelor's Degree or equivalent in Science & Technology, Mathematics, Computer Science / Information Technology, or Engineering (Computer / Telecommunication)

- Model training: Fine-tuning, LoRA, quantization, training data

-RAG configuration: Document preprocessing, chunk embedding, vector DB, LLM interworking

-Deployment/Optimization: ONNX Runtime, NPU Optimization

Optional (Added Advantage):

-On-device AI model optimization experience (Mobile/Embedded, Qualcomm AI)

-Large-scale data learning and distributed learning experience

-Experience using cloud AI platforms (Vertex AI, Azure OpenAI, AWS)