Develop, maintain, and optimize RTL designs using Verilog/SystemVerilog for ASIC/SoC products.
Contribute to architecture definition, microarchitecture development, RTL implementation, and system integration.
Develop UVM-based verification environments, test plans, test cases, SystemVerilog Assertions (SVA), and functional coverage models.
Debug functional, performance, and integration issues across IP, subsystem, and SoC levels.
Design and develop AI agents and copilots to enhance RTL design, verification, debugging, code review, and engineering productivity.
Apply Generative AI, Large Language Models (LLMs), and agentic AI technologies to automate RTL analysis, test plan and assertion generation, coverage analysis, debug triage, and regression failure root-cause analysis.
Build AI-powered engineering workflows using Python, LangChain, MCP tools, vector databases, and modern GenAI platforms.
Collaborate with cross-functional teams, including Architecture, Design, Design Verification (DV), Physical Design, Emulation, Post-Silicon, and Software Engineering, to identify and implement high-impact AI use cases.
Drive AI adoption across silicon engineering teams by developing reusable frameworks, sharing best practices, and establishing effective methodologies.
Mentor engineers in AI-assisted design and verification practices, and evaluate the productivity improvements achieved through AI-enabled solutions.
Requirements & Qualifications
Bachelor's degree or above in Electrical Engineering, Computer Engineering, Computer Science, Microelectronics, or a related discipline
Minimum 3 years of experience in ASIC/SoC RTL Design and/or Design Verification.
At least 1 year of hands-on experience applying AI/ML or Generative AI technologies.
Strong proficiency in Verilog/SystemVerilog and RTL design methodologies.
Solid understanding of ASIC/SoC development flows, UVM-based verification, functional coverage, and SystemVerilog Assertions (SVA).
Practical experience with Generative AI, LLMs, prompt engineering, and AI agent workflows.
Strong Python programming skills, with experience in Linux, Git, and scripting automation.
Familiarity with Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), and AI development frameworks such as LangChain, LangGraph, AutoGen, or CrewAI.
Familiarity with cloud-based AI platforms and AI-assisted software development tools.
Strong analytical, problem-solving, and debugging skills.
Excellent cross-functional communication and collaboration skills, with the ability to work effectively in multi-site teams and manage multiple priorities.