Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Tier-1 brand, metro location, and mid-level experience increase competition but the niche AI-for-verification focus tempers it.
Role demands domain-specific hardware verification and EDA knowledge, making backgrounds from other industries less transferable.
Requires explicit 3+ years plus mandatory ML/LLM and hardware verification expertise, so moderately strict filtering.
Design, develop, and deploy AI/ML-based tools and intelligent agents to automate performance verification workflows to accelerate verification cycles and improve coverage.
Integrate LLM-based assistants into verification infrastructure for natural-language querying of verification data and design data pipelines for continuous AI model training.
Collaborate with verification engineers to validate AI-driven solutions and establish metrics to measure impact such as cycle time reduction and engineer productivity.
B.Tech/M.Tech/PhD in Electrical Engineering, Computer Science, or related field.
3+ years of experience in hardware verification, performance validation, or EDA tool development.
Strong programming skills in Python; familiarity with C/C++, SystemVerilog/UVM is a plus.
Hands-on experience with ML/AI frameworks (PyTorch, TensorFlow, scikit-learn) or LLM APIs (OpenAI, NVIDIA NIM/NeMo).
Experienced in applying ML/AI techniques specifically to EDA or hardware verification challenges (e.g., coverage closure, bug prediction).
Familiarity with advanced AI techniques including RAG architectures, prompt engineering, and agentic AI frameworks like LangChain or CrewAI.
Knowledge of NVIDIA GPU/SoC architecture or equivalent complex hardware platform verification environments, demonstrated by publications or patents in AI-for-verification.