Khalid Hamad Email & Phone Number

Ras Tanura, Eastern, Saudi Arabia

I am working as a Project Engineer in Saleh Hamad Al Qahtani Establishment For General Contracting, a Saudi Aramco-approved construction company. My responsibilities includes, but not limited to:• Supervise and manage the execution of mechanical activities, ensuring compliance with design specifications and quality standards, as well as health and safety regulations.• Act as a project manager for Short-Form Agreement (SFA) projects, managing all phases of a project through the entire life-cycle of the project.• Prepare detailed technical procedures and Job Safety Analysis (JSA) sheets for critical activities and participate in safety and risk assessment meetings.• Conduct site inspections to verify the feasibility of mechanical work, collaborating with client’s team and sub-contractors to resolve technical issues prior to mobilization. • Ensure mechanical work adherence to outlined requirements in project's Scope of Work, Saudi Aramco Engineering Standards (SAES), Saudi Aramco Materials System Specification (SAMSS), Saudi Aramco General Instructions (GIs), Saudi Aramco Engineering Procedures (SAEP), and international standards.• Collaborate with procurement department to ensure procured materials adhere to applicable quality standards and arrive on time.• Solve site issues that arise during the execution phase in a timely manner.

Ras Tanura, Eastern, Saudi Arabia

Al Wakrah, Qatar

• Developed a comprehensive data management system to streamline the organization and accessibility of information related to potential clients, consultancy offices, and contracting companies, resulting in enhanced sales process efficiency and effectiveness.• Established strategic partnerships with seven consultancy offices and twelve contracting companies utilizing strong presentation and negotiation skills, receiving eight new IFBs (Invitation for Bid).

Klang, Selangor, Malaysia

• Reduced the number of failed pallets by the quality assurance department and eradicated the need for manual inspection by developing an innovative defect detection system leveraging deep neural networks to identify defective gloves with remarkable accuracy of 99.98%. The system successfully detects previously undetectable defects.• Designed and built a new rejection mechanism that adapts to variations in production line speed, guaranteeing the complete removal of defective gloves.• Improved the accuracy and efficiency of production line testing by establishing a new data management and collection system and automating calculations using Microsoft Excel spreadsheets. This resulted in a time-saving of approximately 15 minutes per day.