Hassan Nasir

Designed and implemented a real-time anomaly detection pipeline using deep learning and computer vision techniques. Trained MobileNetV2 and PatchCore models on categories from the MVTec anomaly detection dataset. Integrated models into an efficient inference pipeline and experimented with optimization frameworks such as PyTorch FP32, FP16, OpenVINO, and TensorRT. Prepared system checklists and technical documentation for installation, testing, and handover. Collaborated with automation, quality, and electrical departments during commissioning of new production cells.

Analyzed industrial plant layouts and system schematics ensuring compliance with design and operational standards. Created and updated AutoCAD Electrical drawings, improving accuracy by 20% and reducing rework through version control. Supported data-driven decision-making preparing Bill of Materials and technical documentation for cross-departmental teams. Participated in site coordination and technical inspections verifying system connectivity before PLC programming. Collaborated with mechanical and automation teams to maintain system layout consistency and documentation.

Collected and structured technical data for renewable energy projects enabling accurate feasibility assessments. Developed electrical distribution and wiring diagrams supporting relay logic and PLC sensor inputs using AutoCAD. Contributed to site evaluation reports synthesizing observational data and infrastructure requirements. Supported preparation of technical documentation for integration of electrical and control systems in custom-built industrial setups.