Best NanoTech
Website:
bestpeo.com
Company:
https://www.linkedin.com/company/best-nanotech
Seniority: Mid-Senior level
Industries: Semiconductor Manufacturing
Job details:
AI Process Optimization Engineer Semiconductor Manufacturing
Location: Bengaluru / Ahmedabad / Noida/ Pune, India
Work Mode: Onsite
Experience: 6- 15 Years
Industry: Semiconductor Manufacturing | Foundry | OSAT | Artificial Intelligence | Industry 4.0
Role Overview
We are looking for an experienced AI Process Optimization Engineer to drive the adoption of Artificial Intelligence (AI), Machine Learning (ML), and Advanced Analytics across semiconductor manufacturing operations. This role is responsible for developing intelligent process optimization solutions that improve yield, process capability, cycle time, equipment utilization, defect reduction, energy efficiency, and manufacturing productivity.
The successful candidate will work closely with Process Integration, Lithography, Etch, CMP, Thin Films, Diffusion, Implant, Wet Process, Metrology, Equipment Engineering, Yield Engineering, Factory Automation, Manufacturing, and Data Science teams to build AI-driven optimization models supporting advanced wafer fabrication and semiconductor packaging operations.
This is a strategic role at the intersection of Semiconductor Manufacturing, Data Science, Artificial Intelligence, Digital Twin, and Smart Factory transformation.
Key Responsibilities
- Design and deploy AI-driven process optimization solutions for semiconductor wafer fabrication and advanced packaging.
- Develop Machine Learning models to optimize critical manufacturing processes including Lithography, Etch, CMP, Diffusion, Implant, Thin Films, Wet Process, CVD, PVD, and Packaging.
- Analyze large-scale manufacturing datasets to identify process variation, bottlenecks, yield loss mechanisms, and optimization opportunities.
- Develop predictive process control models to improve yield, throughput, cycle time, and Overall Equipment Effectiveness (OEE).
- Apply AI techniques to optimize process recipes, equipment parameters, and manufacturing workflows.
- Collaborate with Process Integration, Equipment, Manufacturing, Quality, Yield, Metrology, and Automation teams to implement intelligent manufacturing solutions.
- Build Digital Twin models for semiconductor manufacturing processes and production optimization.
- Develop AI-based anomaly detection, root cause analysis, and process health monitoring solutions.
- Integrate AI applications with MES, SPC, APC, FDC, Factory Automation Systems, and manufacturing databases.
- Validate AI models using production data and continuously improve model accuracy and business impact.
- Develop dashboards and analytics for engineering and manufacturing leadership.
- Support Industry 4.0 and Smart Factory initiatives across global semiconductor manufacturing operations.
Required Qualifications
- Bachelor's or Master's degree in Electronics Engineering, Computer Science, Artificial Intelligence, Data Science, Industrial Engineering, Mechanical Engineering, Chemical Engineering, Materials Science, or related discipline.
- 6 15 years of experience in Semiconductor Manufacturing, Process Engineering, AI/ML Engineering, Data Analytics, or Digital Manufacturing.
- Strong understanding of semiconductor manufacturing processes and process integration.
- Proven experience applying AI or Machine Learning to manufacturing environments.
Technical Skills Semiconductor Manufacturing
- Wafer Fabrication
- Process Integration
- Lithography
- Dry Etch
- Wet Etch
- CMP
- Thin Films
- CVD
- PVD
- Diffusion
- Ion Implantation
- Cleaning Process
- Metrology
- Yield Engineering
- Advanced Packaging
- Semiconductor Test
Artificial Intelligence & Machine Learning
- Machine Learning
- Deep Learning
- Predictive Analytics
- Time Series Forecasting
- Computer Vision
- Reinforcement Learning
- Generative AI
- Large Language Models (LLMs)
- AI Agents
- Optimization Algorithms
Manufacturing Analytics
- Statistical Process Control (SPC)
- Fault Detection & Classification (FDC)
- Advanced Process Control (APC)
- Root Cause Analysis
- Yield Optimization
- Process Capability (Cp/Cpk)
- Design of Experiments (DOE)
- OEE Analysis
- Digital Twin
- Predictive Maintenance
Programming & Data Science
- Python
- SQL
- PySpark
- Pandas
- NumPy
- Scikit-learn
- TensorFlow
- PyTorch
- MLflow
- Databricks
Cloud & Infrastructure
- Microsoft Azure
- AWS
- Google Cloud Platform
- Docker
- Kubernetes
- Snowflake
- Apache Spark
Visualization & Reporting
- Power BI
- Tableau
- Grafana
- Kibana
- Excel
#LI-SD1
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