AI Engineering Architect – Agentic AI & Digital Engineering
$186,200–$316,500 year
On-site · Milpitas, California, United States
Job Summary
The AI Engineering Architect at KLA is responsible for owning the AI roadmap for EUV source and digital engineering use cases, prioritizing high-value opportunities tied to engineering impact. Key responsibilities include building AI solutions for source health analytics, anomaly detection, and predictive monitoring; developing AI-assisted workflows for engineering task automation; and partnering with engineering teams to support AI-assisted modeling and digital transformation. The role requires a doctorate or advanced degree in related fields and significant experience in designing and deploying AI/ML solutions within engineering contexts.
Required Qualifications
- Doctorate (Academic) Degree and related work experience of 5 years
- Master's Level Degree and related work experience of 8 years
- Bachelor's Level Degree and related work experience of 12 years
- Master’s degree or PhD in Computer Science, AI/ML, Data Science, Electrical Engineering, Mechanical Engineering, Physics, Systems Engineering, or a related field
- Strong hands-on experience building and deploying AI/ML solutions for complex engineering or industrial workflows
- Experience with integrating AI into engineering data, software, or workflow systems
- Experience with engineering data such as sensor streams, logs, structured/unstructured technical documents, or time-series datasets
- Ability to translate ambiguous engineering pain points into scalable AI architectures and deployable solutions
Desired Qualifications
- Experience with agentic AI, tool-using LLM workflows, multi-agent systems, or RAG/vector-backed knowledge systems
- Experience with time-series forecasting, anomaly detection, or predictive analytics for industrial systems
- Experience with MBSE, digital thread, SysML, requirements traceability, or model-centric engineering
- Experience with CAD/PLM/PDM integration, BOM workflows, or engineering data interoperability
- Experience in semiconductor equipment, capital equipment, optics, plasma, vacuum systems, or manufacturing engineering
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