About the role
Senior-level Data & AI Architect responsible for designing and implementing AI-driven data architectures across semiconductor and advanced manufacturing environments. Leads cross-functional teams to deploy machine-learning models, real-time analytics, and digital-twin solutions that optimize yield, predictive maintenance, and operational efficiency.
BankingOnsite
Key Responsibilities
- Architect end-to-end data and AI solutions spanning edge devices, on-prem systems, and cloud platforms
- Design scalable data lakes, warehouses, and real-time streaming pipelines for fab and assembly/test data
- Develop and deploy machine-learning models for predictive maintenance, yield optimization, and process control
- Lead AI strategy and roadmap aligned with semiconductor manufacturing KPIs
- Partner with process, equipment, and IT teams to integrate AI insights into MES, ERP, and SCADA systems
- Establish data governance, security, and compliance frameworks across global manufacturing sites
- Mentor data scientists and engineers; drive best practices in MLOps and model lifecycle management
- Present AI business cases and ROI to executive leadership and external customers
Requirements
- Bachelor's or Master's in Computer Science, Data Science, Electrical/Industrial Engineering or related field
- 8+ years of experience architecting data and AI solutions in semiconductor or high-tech manufacturing
- Proven track record deploying ML models in production at scale using TensorFlow, PyTorch, or equivalent
- Expert-level proficiency in Python, SQL, and cloud platforms (AWS, Azure, or GCP)
- Deep understanding of semiconductor manufacturing processes, SPC, and yield management systems
- Experience with real-time data ingestion from PLC, SCADA, and MES systems
- Strong knowledge of data modeling, ETL/ELT, and big-data technologies (Spark, Kafka, Kubernetes)
- Demonstrated ability to lead cross-functional teams and manage vendor relationships
- Excellent communication skills with experience presenting technical concepts to non-technical stakeholders
- Willingness to travel up to 25% to global manufacturing sites
- Preferred: PhD in relevant field, certifications in cloud AI/ML, and experience with digital-twin or edge analytics