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Tier-1 employer, metro location, mid-level AI role, and broad required skills drive high competition.
Core ML, LLM, cloud, and distributed systems skills transfer across industries, but semiconductor-specific domain reduces fit slightly.
Explicit years, mandatory programming and ML/cloud stack, and platform experience indicate high shortlisting strictness.
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Design and implement AI/ML applications and pipelines integrating LLMs, GenAI, and custom models to enhance chip design and debug engineering workflows.
Build scalable distributed systems, cloud-native backend services, and user interfaces tailored for engineering productivity enhancement.
Develop robust ETL pipelines, data ingestion and transformation processes, and collaborate across teams to deliver high-quality AI-driven engineering tools.
Bachelor's degree in Computer Science, Electrical/Electronics Engineering, or related field with 3+ years of hardware or related engineering experience; OR Master's degree with 2+ years; OR PhD with 1+ year experience.
Strong programming skills in Python and C/C++ with expertise in data structures, algorithms, and software design principles.
Experience in building scalable software systems, software architecture, and design patterns.
Experience or familiarity with data analysis libraries (Pandas, NumPy), SQL/NoSQL databases, ETL frameworks, cloud platforms (e.g., AWS), and knowledge of AI/ML techniques including LLMs, GenAI, and ML frameworks.
Experienced in AI/ML integration within engineering workflows, particularly in chip design and debug environments.
Skilled in designing and deploying distributed cloud-native applications and building scalable backend and ETL pipelines.
Demonstrates ability to deliver complex software solutions independently while collaborating effectively with cross-functional teams.