AI application engineer

Job Responsibilities

-Design and develop enterprise-grade AI application systems, driving the deep integration of AI into manufacturing

and business operations. -Build systems—such as intelligent quality inspection, predictive maintenance, knowledge Q&A, and intelligent

production scheduling—leveraging Large Language Models (LLMs), machine learning, and computer vision technologies. -Oversee AI model deployment, system performance optimization, and stability assurance; ensure system scalability

and sustainable operation; and resolve issues related to performance bottlenecks and model accuracy degradation. -Develop enterprise-level AI capability platforms, including AI agent systems, RAG knowledge bases, and centralized

data and capability platforms. -Perform model compression and performance optimization to meet specific performance and quantization requirements. -Collaborate across teams to ensure the stable implementation of model optimization solutions within products. -Engage with clients and product teams to identify new business use cases and value opportunities through

Proof-of-Concept (POC) projects and rapid experimentation. -Produce technical documentation, experiment reports, and methodological summaries to support team decision-making

and knowledge accumulation.

Requirements

-Master’s degree or higher in Computer Science, Machine Learning, Electronic Information, Intelligent Manufacturing,

or related fields; -2+ years of experience in AI development or related areas; experience implementing AI projects in the semiconductor

manufacturing industry is a plus; -Proficiency in Python and familiarity with common machine learning libraries (e.g., NumPy, Pandas, scikit-learn, and at

least one of PyTorch or TensorFlow); strong coding and engineering mindset; -Familiarity with application development using mainstream Large Language Models (e.g., OpenAI, Tongyi, GPT) and

prompt engineering; -Familiarity with RAG architecture and vector databases (e.g., Milvus, FAISS, Weaviate); -Familiarity with LLM application frameworks (e.g., LangChain, LlamaIndex, Dify) is a plus; -Familiarity with cutting-edge technologies such as Transformers, LLMs, and vLLM; -Backend development skills (e.g., FastAPI, Flask, Django) and API system design capabilities; -Familiarity with Docker and Linux deployment environments; experience with local model deployment is a plus; -Strong problem-definition skills and an exploratory mindset; ability to formulate hypotheses, design experiments,

and validate feasibility in scenarios with limited precedents; -Excellent communication and cross-team collaboration skills; ability to drive projects forward in coordination with

frontend/backend teams, SRE, product managers, and QA; -CET-6 or equivalent English proficiency (reading/writing); ability to read technical documentation in English and handle

basic communication; -Interest in intelligent applications for semiconductor testing and a willingness to explore and implement new application

directions with the team.