Back
Tier-1 brand, metro location, and mid-level AI role drive high applicant competition.
Requires specialized agentic AI, RAG, vector DB, and HPC infrastructure expertise, reducing cross-industry transferability.
Explicit 4–6 years requirement and many mandatory AI and infrastructure skills create strict shortlisting.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Develop and deploy AI-powered infrastructure solutions, including AI assistants, RAG systems, and autonomous AI agents for semiconductor design workloads.
Automate engineering operations workflows such as troubleshooting, incident management, and root-cause analysis in collaboration with EDA/HPC infrastructure teams.
Integrate and maintain AI solutions across cloud (AWS) and on-premises infrastructure environments involving compute, storage, networking, and Linux systems.
Bachelor's or Master's degree in Computer Science, IT, AI, Data Science, or related field.
4-6 years of professional experience in software development, specifically with Python and API development.
Experience designing and implementing Agentic AI applications, AI chatbots, and RAG systems using frameworks like LangChain and LangGraph.
Strong Linux administration skills plus infrastructure knowledge across compute, storage, networking, distributed systems, and cloud platforms like AWS; experience with vector databases and semantic search.
Experienced in building enterprise-grade AI/ML solutions that enhance infrastructure operations in semiconductor or similar high-compute environments.
Proficient in agentic AI workflows including reasoning, planning, tool execution, and autonomous remediation.
Operates effectively within hybrid cloud and on-prem Linux-based infrastructure supporting complex engineering workloads with a focus on automation and AI-driven productivity.