Douglasville, Georgia, United States
Currently looking to utilize my skills from a Master's in Artificial Intelligence at Kennesaw State University and an MIT certification in Applied Data Science in Machine Learning , my ambition is to harness AI and robotics to drive innovation, efficiency, and sustainable practices. I am committed to lifelong learning and value teamwork in shaping the future of automation.
Key Responsibilities: Design and Development: Developing and implementing electrical control systems for automation projects. System Maintenance: Troubleshooting, repairing, and improving existing control systems. PLC Programming: Writing and modifying PLC programs for various automation tasks. HMI Development: Designing and configuring HMI interfaces for monitoring and controlling systems. Project Management: Supporting the implementation of new capital projects and EIC projects (Electrical and Instrumentation Controls). Collaboration: Working with internal and external partners, including maintenance teams and vendors. Process Improvement: Contributing to continuous improvement initiatives by leveraging automation and robotics Robot Development: Modifying pick and place process for automation and robotics
Developed optimized control programs using Python improving production efficiency by 20%. Designed robotic material handling solutions for palletizing and case loading, reducing cycle time by 15%. Integrated AI-driven process monitoring, enhancing system diagnostics and reducing downtime. Conducted data-driven performance analysis on robotic systems, leading to targeted efficiency improvements. Implemented machine learning algorithms to optimize robotic path planning, reducing error rates by 25%.
Led predictive maintenance initiatives by integrating ML-based analysis, decreasing equipment failures by 25%. Implemented AI-driven fault detection to anticipate system failures, reducing downtime. Developed machine learning models to optimize control parameters, increasing throughput by 10%
Spearheaded robotic automation improvements, increasing production efficiency by 18%. Implemented AI-based fault detection algorithms, reducing equipment downtime by 30%. Conducted data analytics on production cycles, optimizing equipment performance. Led AI-assisted predictive maintenance initiatives, decreasing unexpected equipment failures by 20%.