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Specialized LLM/agent skills but mid-level experience and modest brand produce moderate competition.
Strong ML/AI skill transferability, but domain-specific engineering/semiconductor exposure increases sensitivity.
Explicit 2-4 years, mandatory LLM/agent experience and language requirements enforce strict screening.
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Develop AI-assisted workflows to automate and enhance semiconductor engineering processes including setup, troubleshooting, review, and knowledge retrieval.
Integrate AI agents and tool-using agents with platform APIs, engineering data, documents, and automation workflows to provide actionable and testable assistance.
Build and maintain evaluation datasets and workflow templates to standardize engineering tasks, verify AI assistant accuracy, and ensure traceable, reviewable results.
2-4 years of professional experience in software development or AI engineering.
Strong programming skills in Python or TypeScript.
Hands-on experience with LLMs (large language models), retrieval-augmented generation, tool calling, agents, or workflow automation, including API integration.
Proven ability to develop practical software solutions (not just prototypes) with good debugging, testing, and documentation practices.
Experienced in building AI-based automation tools or engineering assistants connected to real-world engineering data and platforms.
Familiar with vector databases, agent frameworks, and AI workflow evaluation techniques such as prompt management and retrieval evaluation.
Comfortable working in semiconductor engineering or related automation domains, with a practical mindset for reliable integration and validation of AI systems.