The Role
Design and implement enterprise AI applications (LLMs, CV, ML) for manufacturing: intelligent inspection, predictive maintenance, RAG/knowledge bases, model deployment, optimization, and POC collaboration with product teams. Ensure scalable, stable production systems and produce technical documentation and experiment reports.
Summary Generated by Built In
Job Summary & ResponsibilitiesJob 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.Preferred Qualifications
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.Preferred Qualifications
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.
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.
Skills Required
- 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
- Proficiency in Python and strong coding and engineering mindset
- Familiarity with common machine learning libraries (NumPy, Pandas, scikit-learn)
- Experience with at least one of PyTorch or TensorFlow
- Familiarity with application development using mainstream LLMs (OpenAI, Tongyi, GPT) and prompt engineering
- Familiarity with RAG architecture and vector databases (Milvus, FAISS, Weaviate)
- Familiarity with LLM application frameworks (LangChain, LlamaIndex, Dify)
- Familiarity with Transformers, LLMs, and vLLM
- Backend development skills (FastAPI, Flask, Django) and API system design capabilities
- Familiarity with Docker and Linux deployment environments; experience with local model deployment
- Experience with model compression and performance optimization
- Strong problem-definition, experimental design, and validation skills
- Excellent communication and cross-team collaboration skills
- CET-6 or equivalent English proficiency (reading/writing)
- Interest in intelligent applications for semiconductor testing and willingness to explore new directions
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The Company
What We Do
For over a half-century, Advantest has been designing innovative electronic measuring equipment and semiconductor test systems essential to the development and manufacture of advanced computer and telecommunications products. On April 1, 2012, Advantest completed its integration of Verigy Ltd. Additional Information about Advantest can be found at www.advantest.com.
