Semiconductor Knowledge Graph & LLM Engineer

Engineering Knowledge Management | Generative AI | Large Language Models

Location: Bengaluru / Hyderabad / Pune / Noida, India

Work Mode: Onsite

Experience: 6 -15 Years

Industry: Semiconductor | Artificial Intelligence | EDA | Knowledge Engineering

Role Overview

We are seeking an experienced Semiconductor Knowledge Graph & LLM Engineer to develop next-generation AI-powered engineering knowledge platforms for semiconductor design, manufacturing, and product development. This role focuses on building intelligent systems that enable engineers to instantly access technical knowledge across RTL design, Physical Design, Verification, DFT, Process Engineering, Manufacturing, Failure Analysis, Product Engineering, and Semiconductor R&D.

The successful candidate will design Knowledge Graphs, Retrieval-Augmented Generation (RAG) pipelines, Large Language Model (LLM) applications, AI Agents, and enterprise search platforms that transform engineering documentation, design history, silicon learning, manufacturing data, patents, and technical standards into actionable engineering intelligence.

This role sits at the intersection of Semiconductor Engineering, Artificial Intelligence, Knowledge Management, and Enterprise Digital Transformation.

Key Responsibilities

• Design and develop AI-powered engineering knowledge platforms for semiconductor organizations.
• Build enterprise Knowledge Graphs connecting design documents, specifications, simulation data, silicon validation reports, manufacturing records, patents, engineering change orders (ECOs), failure analysis reports, and technical documentation.
• Develop Retrieval-Augmented Generation (RAG) pipelines for engineering search and intelligent question-answering.
• Build AI assistants enabling engineers to retrieve design knowledge, debug history, process learnings, yield issues, and best practices.
• Integrate Large Language Models (LLMs) with engineering repositories, PLM systems, document management systems, Git repositories, and EDA databases.
• Develop semantic search engines using vector databases and embedding models.
• Design AI Agents that automate engineering documentation, report generation, design reviews, knowledge extraction, and engineering workflows.
• Collaborate with Design, Verification, CAD, Manufacturing, Quality, Product Engineering, Reliability, and IT teams to define enterprise AI knowledge strategies.
• Develop APIs and data pipelines connecting engineering tools, manufacturing systems, and enterprise applications.
• Evaluate emerging AI technologies for enterprise knowledge management and semiconductor engineering productivity.
• Ensure security, governance, access control, and responsible AI practices for engineering knowledge systems.
• Prepare technical documentation, architecture designs, and deployment strategies.Required Qualifications
• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Electronics Engineering, Computer Engineering, Data Science, Information Systems, or related discipline.
• 6 15 years of experience in AI Engineering, Enterprise Search, Knowledge Engineering, Data Engineering, Software Development, or Semiconductor Engineering.
• Strong understanding of semiconductor engineering workflows and engineering documentation.
• Experience building AI or enterprise knowledge management platforms.
• Strong programming and system architecture skills.Technical Skills Artificial Intelligence
• Large Language Models (LLMs)
• Generative AI
• Retrieval-Augmented Generation (RAG)
• AI Agents
• Prompt Engineering
• Agentic AI
• Semantic Search
• Embedding Models
• Knowledge GraphsKnowledge Management
• Enterprise Knowledge Management
• Engineering Knowledge Systems
• Document Intelligence
• Semantic Search
• Ontology Development
• Metadata Management
• Taxonomy Design
• Information RetrievalProgramming & Development
• Python
• Java
• SQL
• REST APIs
• GraphQL
• Git
• LinuxAI Frameworks
• LangChain
• LlamaIndex
• Hugging Face
• OpenAI APIs
• NVIDIA NeMo
• NVIDIA NIM
• MCP (Model Context Protocol)
• CrewAI
• AutoGenVector Databases & Search
• Pinecone
• Weaviate
• ChromaDB
• Milvus
• Elasticsearch
• OpenSearch
• FAISSKnowledge Graph Technologies
• Neo4j
• RDF
• SPARQL
• Graph Databases
• Ontology Engineering
• GraphRAGCloud & Infrastructure
• AWS
• Microsoft Azure
• Google Cloud Platform
• Docker
• Kubernetes
• MLflowSemiconductor Engineering Knowledge
• ASIC Design Flow
• RTL Design
• Physical Design
• Design Verification
• DFT
• Process Integration
• Yield Engineering
• Manufacturing
• Failure Analysis
• Product Engineering
• EDA Workflows