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Tier-1 brand and Bangalore metro increase applicant density; specialized ML and semiconductor focus reduces applicant pool.
ML/AI work tightly integrated with semiconductor engineering limits cross-industry transferability.
Explicit 7+ years plus mandatory deep learning, LLM, and GPU/CUDA expertise enforces strict filters.
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Design and implement end-to-end data analytics and ML workflows specifically for semiconductor engineering applications.
Develop, train, and optimize machine learning models including supervised, unsupervised, deep learning, and GenAI/LLM techniques.
Build scalable data pipelines and collaborate with cross-functional teams to integrate AI models into chip design, qualification, and debug processes.
Bachelor’s or Master’s degree in Computer Science, Data Science, Electrical/Electronics Engineering, Engineering, or related field with minimum 7+ years work experience.
Strong programming skills in Python or C++ with expertise in ML frameworks like TensorFlow, PyTorch, or scikit-learn.
Experience with supervised, unsupervised, deep learning (RNNs, Transformers), feature engineering, statistical and time series analysis (ANOVA).
Work Experience Required: Minimum 7+ years (varies slightly by degree level with hardware engineering experience).
Experienced in leading development of advanced AI/ML models tailored to semiconductor or hardware engineering domains.
Proficient in statistical analysis, scalable data engineering, and GenAI technologies including LLM fine-tuning and RAG optimization.
Able to architect software solutions integrating ML models with hardware engineering workflows, showing strong system design capability.