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Semiconductor AI Manufacturing Analytics Engineer
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1 month ago
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Important information
Contract type:
Permanent
Location:
Bengaluru, Karnataka, India
Work mode:
Onsite
Published on:
9 September 2026
What they need
AI, Machine Learning & Smart Manufacturing Analytics
Location: Bengaluru / Ahmedabad / Dholera / Sanand, India
Work Mode: Onsite / Hybrid
Experience: 5- 12 Years
Industry: Semiconductor Manufacturing | Foundry | OSAT | AI | Smart Factory
Role Overview
We are seeking an experienced Semiconductor AI Manufacturing Analytics Engineer to develop and deploy Artificial Intelligence (AI), Machine Learning (ML), and Advanced Analytics solutions that improve semiconductor manufacturing performance. The role focuses on applying AI to yield enhancement, process optimization, predictive maintenance, defect detection, statistical process control (SPC), fault detection and classification (FDC), advanced process control (APC), and smart factory initiatives .
The successful candidate will work closely with Manufacturing, Process Integration, Equipment Engineering, Yield Engineering, Quality, Data Engineering, and Digital Transformation teams to build AI-powered manufacturing solutions that improve productivity, equipment utilization, product quality, and manufacturing efficiency.
Key Responsibilities
- Develop AI and Machine Learning models for semiconductor manufacturing and process optimization.
- Build predictive analytics solutions for yield improvement, defect prediction, equipment health monitoring, and process control.
- Analyze large-scale manufacturing datasets from wafer fabrication, assembly, packaging, and semiconductor test operations.
- Design AI-driven solutions for Statistical Process Control (SPC), Fault Detection & Classification (FDC), and Advanced Process Control (APC).
- Develop predictive maintenance models for semiconductor manufacturing equipment to reduce downtime and improve Overall Equipment Effectiveness (OEE).
- Build AI-based root cause analysis frameworks using manufacturing, metrology, and equipment data.
- Collaborate with Process, Equipment, Yield, Manufacturing, Quality, and Automation teams to identify digital transformation opportunities.
- Develop dashboards, KPIs, and visualization tools for engineering and factory leadership.
- Integrate AI applications with MES, Manufacturing Data Systems, Equipment Automation, and Factory Information Systems.
- Deploy AI models into production environments while monitoring performance and continuous improvement.
- Support Digital Twin initiatives for semiconductor manufacturing.
- Document AI methodologies, validation reports, and technical recommendations.
Required Qualifications
- Bachelor's or Master's degree in Electronics Engineering, Computer Science, Data Science, Artificial Intelligence, Industrial Engineering, Mechanical Engineering, or related discipline.
- 5 12 years of experience in Semiconductor Manufacturing, Manufacturing Analytics, AI/ML Engineering, or Industrial Data Analytics.
- Strong understanding of semiconductor manufacturing processes and factory operations.
- Experience applying AI or Machine Learning to industrial or manufacturing environments.
Technical Skills Semiconductor Manufacturing
- Wafer Fabrication
- Semiconductor Assembly
- Advanced Packaging
- Semiconductor Test
- Manufacturing Operations
- Yield Engineering
- Process Integration
- Equipment Engineering
- Smart Factory
Manufacturing Analytics
- Statistical Process Control (SPC)
- Fault Detection & Classification (FDC)
- Advanced Process Control (APC)
- Overall Equipment Effectiveness (OEE)
- Root Cause Analysis
- Yield Analytics
- Process Monitoring
- Equipment Analytics
- Manufacturing KPIs
Artificial Intelligence & Machine Learning
- Machine Learning
- Deep Learning
- Predictive Analytics
- Computer Vision
- Anomaly Detection
- Time Series Forecasting
- Reinforcement Learning (Preferred)
- Generative AI
- Large Language Models (LLMs)
- AI Agents
Programming & Data Engineering
- Python
- SQL
- PySpark
- Pandas
- NumPy
- Scikit-learn
- TensorFlow
- PyTorch
- Databricks
- Apache Spark
Cloud & Infrastructure
- Microsoft Azure
- AWS
- Google Cloud Platform
- Snowflake
- Docker
- Kubernetes
- MLflow
Visualization & BI
- Power BI
- Tableau
- Grafana
- Kibana
- Excel
#LI-SD1
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