AI Engineering Productivity Manager – Semiconductor

Best NanoTech; Bengaluru; Hybrid; INR 4,000,000 - 7,000,000; Full time

Job summary

Best NanoTech in Bengaluru/Bharat is seeking an AI Engineering Productivity Manager to lead AI adoption across semiconductor engineering teams, improving productivity, accelerating development, and enabling intelligent automation.

The role covers Generative AI, LLMs, AI Agents, Engineering Copilots, and Workflow Automation across RTL Design, Physical Design, Verification, and CAD, aiming for measurable productivity gains.

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.

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.

Education

Engineering degree

MBA advantage

Job description

AI Engineering Productivity Manager – Semiconductor

AI Engineering Productivity Manager Semiconductor Engineering Excellence | AI Transformation | Developer Productivity

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.