Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Strong Tier-1 brand and metro hiring increase applicant density, tempered by senior specialized AI requirements.
Core ML/LLM engineering skills are highly transferable across industries, so background sensitivity is low.
Explicit 7-8+ years experience bands, advanced degree options, and specific ML/LLM tech stack make filters strict.
Develop and prototype machine learning and deep learning models for semiconductor manufacturing processes using Python and ML frameworks like PyTorch or TensorFlow.
Implement and optimize advanced AI techniques including CNNs, RNNs, Transformers, and work with large language models (LLMs) integrating vector databases and prompt engineering.
Collaborate across teams to apply AI solutions for wafer surface quality and defect inspection, impacting yield analysis and semiconductor fabrication monitoring.
Doctorate degree with 5 years, Master's degree with 8 years, or Bachelor's degree with 7 years of experience in building AI systems/solutions involving Machine Learning, Deep Learning, and LLMs.
Strong proficiency in Python programming, data structures, algorithms, and machine learning libraries such as NumPy, Pandas, and Scikit-learn.
Experience with ML frameworks (PyTorch or TensorFlow), familiarity with Hugging Face Transformers, vector databases (Milvus, FAISS, Pinecone, ElasticSearch).
Knowledge of SQL, data wrangling, Git/GitHub, Jupyter, and basic Docker usage.
Experienced AI/ML engineer with a deep understanding of applied machine learning and deep learning techniques specifically for industrial semiconductor processes.
Able to manage end-to-end AI solution development including prototyping, model tuning, and integration with state-of-the-art LLM and vector search technologies.
Strong communicator capable of explaining complex ML and LLM concepts and trade-offs clearly to multidisciplinary teams, with a problem-solving and ownership-oriented approach.