Execute FGI and Non-FG shipments in accordance with operational procedures, ensuring on-time delivery to customers (HDD plants), inter-company, external bonded warehouses, and suppliers.
Perform cleanroom environmental surveys and area buy-offs, including Filter Fan Unit surface velocity and leak scan, airborne particle counts and surface contamination sampling.
Serve as the primary Talent Development and Inclusion partner for APAC, representing the function across all regional locations and serving as a trusted advisor to senior business and HR leaders.
Deep Learning Systems Architecture & Ownership : Design and own CNN, U-Net, and ViT systems for precision inspection, measurement, and defect classification. Own real-time anomaly detection from sensor and time-series product development data streams. Highest day-to-day output responsibility.
Deep Learning Model Implementation & Product Development Ownership: Build, train, evaluate, and maintain CNN/U-Net/ViT models for automated inspection and measurement. Own model performance end-to-end — ablation studies, confidence calibration, product development performance monitoring.
Feature Engineering Pipelines: Build and maintain reliable, versioned feature engineering pipelines that transform raw engineering, sensor, and operational data into structured ML-ready feature sets — delivered to the specification defined
Area A — Scientific ML & PINNs Methodology Origination: Originate and advance PINNs methodology — design physics-constrained loss function architectures, validate digital twin ML components against domain physics (with storage domain expert), and deliver validated prototypes with complete technical documentation to implement. As the sole PINNs methodology originator on the team — this capability cannot be delegated or substituted.
Provide dedicated, white-glove IT support to C-Suite executives, resolving technology issues quickly, professionally, and with minimal disruption to their day
Experiment Tracking & Evaluation Reporting: Own MLflow experiment logging for assigned team model runs; conduct model evaluations using standard metrics; produce structured evaluation reports reviewed by team. Your reports directly inform model iteration decisions.