Best NanoTech
Website:
bestpeo.com
Company:
https://www.linkedin.com/company/best-nanotech
Seniority: Mid-Senior level
Industries: Semiconductor Manufacturing
Job details:
AI Engineering Productivity Manager – Semiconductor
AI Engineering Productivity Manager Semiconductor Engineering Excellence | AI Transformation | Developer Productivity
Location: Bengaluru / Hyderabad / Pune / Noida, India
Work Mode: Onsite / Hybrid
Experience: 10- 18 Years
Industry: Semiconductor | Artificial Intelligence | Engineering Productivity | Digital Transformation
Role Overview
We are seeking an experienced AI Engineering Productivity Manager to lead the adoption of Artificial Intelligence across semiconductor engineering organizations with the objective of improving engineering productivity, accelerating product development, and enabling intelligent automation.
This strategic leadership role focuses on applying Generative AI, Large Language Models (LLMs), AI Agents, Engineering Copilots, Knowledge Management, and Workflow Automation across RTL Design, Physical Design, Verification, DFT, Process Engineering, Manufacturing, Test, Product Engineering, CAD, and Software Development teams.
The successful candidate will work with Engineering Leadership, Digital Transformation, IT, AI/ML, CAD, EDA, Product Engineering, Manufacturing, and Operations teams to build an AI-first engineering ecosystem that significantly improves productivity, quality, collaboration, and innovation.
Key Responsibilities
- Define and execute enterprise-wide AI Engineering Productivity strategy for semiconductor engineering organizations.
- Identify engineering workflows that can be accelerated using Generative AI, LLMs, AI Agents, and intelligent automation.
- Lead deployment of AI Engineering Copilots supporting RTL Design, Physical Design, Verification, DFT, Process Engineering, Manufacturing, Test, and Product Engineering.
- Develop AI-enabled solutions for code generation, design documentation, engineering search, report generation, debugging, design reviews, knowledge reuse, and technical documentation.
- Collaborate with EDA, CAD, Design, Manufacturing, Data Engineering, and Software teams to integrate AI into existing engineering workflows.
- Define engineering productivity KPIs including design cycle reduction, automation coverage, engineering efficiency, knowledge reuse, and development velocity.
- Lead adoption of AI-powered engineering platforms including Knowledge Graphs, RAG, Enterprise Search, AI Agents, and Digital Engineering tools.
- Build governance frameworks for AI adoption, security, IP protection, compliance, and responsible AI usage.
- Evaluate emerging AI technologies and identify opportunities to improve semiconductor engineering operations.
- Drive AI change management, user adoption, training, and continuous improvement initiatives.
- Partner with executive leadership to define long-term AI engineering roadmap and digital transformation strategy.
- Mentor cross-functional engineering teams and promote AI best practices across the organization.
- Measure business impact using engineering productivity, quality, cost, and innovation metrics.
Required Qualifications
- Bachelor's or Master's degree in Electronics Engineering, Electrical Engineering, Computer Science, Artificial Intelligence, Software Engineering, or a related discipline. MBA is an advantage.
- 10 18 years of experience in Semiconductor Engineering, Engineering Management, Digital Transformation, AI Platforms, EDA, CAD, or Enterprise Software.
- Strong understanding of semiconductor product development lifecycle.
- Proven experience leading engineering productivity or enterprise transformation initiatives.
- Experience implementing AI technologies within engineering organizations is highly desirable.
Technical Skills Artificial Intelligence
- Generative AI
- Large Language Models (LLMs)
- AI Agents
- Agentic AI
- Retrieval-Augmented Generation (RAG)
- Prompt Engineering
- Engineering Copilots
- Knowledge Graphs
- AI Workflow Automation
- Enterprise AI Platforms
Semiconductor Engineering
- RTL Design
- ASIC / SoC Design
- Physical Design
- Design Verification
- DFT
- Static Timing Analysis
- EDA Automation
- Manufacturing Engineering
- Yield Engineering
- Product Engineering
- CAD Methodology
- Semiconductor Design Flow
Engineering Productivity
- Workflow Automation
- Engineering Analytics
- Digital Engineering
- Knowledge Management
- Process Improvement
- Lean Engineering
- Agile Development
- DevOps
- Engineering Metrics
- Continuous Improvement
Programming & Integration
- Python
- SQL
- Java
- REST APIs
- Git
- Linux
- Shell Scripting
AI Frameworks
- LangChain
- LlamaIndex
- Hugging Face
- OpenAI APIs
- NVIDIA NeMo / NIM
- CrewAI
- AutoGen
- MLflow
- Vector Databases
Cloud & Infrastructure
- AWS
- Microsoft Azure
- Google Cloud Platform
- Kubernetes
- Docker
- Databricks
- Snowflake
- CI/CD
- MLOps
- LLMOps
Enterprise Platforms
- Jira
- Confluence
- GitHub
- GitLab
- SharePoint
- ServiceNow
- Engineering Knowledge Platforms
- PLM
- MES
- ERP
#LI-SD1
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