My name is Abdul Muqtadir Mohammed. With over 11 years of professional experience in pioneering AI-driven architectures focused on cloud infrastructure and intelligent supply chains, coupled with a strong academic background, I am confident that I can contribute meaningfully to the review process. 

 

Professional and Academic Highlights: 

Industry Expertise: Senior Software Engineer with hands-on experience at industry-leading companies such as Amazon, Butternut AI, Amazon Web Services (AWS), and S&P Capital IQ. 

 

Academic Excellence: 

 

Master of Science in Artificial Intelligence, Computer Science and Engineering – State University of New York. 

 

Bachelor of Engineering in Computer Science – Osmania University, Merit List Recipient. 

 

My combination of industry expertise and academic foundation equips me with the skills to evaluate submissions with a keen understanding of their technical depth, practical impact, and alignment with current industry trends. 

 

Key Professional Contributions: 

Amazon | Ship With Amazon (SWA): 

I developed an AI-driven service time estimation model for the SWA program, enabling third-party businesses to leverage Amazon’s logistics network. This model used regression-based machine learning to improve delivery time predictions, optimize routing, and maximize fleet productivity. My work delivered $60 million in cost savings across North America and Europe by reducing delays and enhancing operational efficiency, establishing Amazon’s SWA program as a leader in logistics innovation. 

 

Butternut AI | Website Generation Platform: 

As Co-Founder and CTO, I led the development of an AI-powered platform capable of generating fully functional websites in under 20 seconds from simple user prompts. The platform democratized web development for 400,000+ global users, eliminating the need for coding expertise. Recognized as a Top Product on Product Hunt, Butternut AI secured $1M+ in venture capital funding, setting a new standard for AI-driven web development. 

 

AWS | Attribute-Based Instance Type Selection: 

At AWS, I contributed to the Attribute-Based Instance Type Selection feature for EC2 Auto Scaling, EC2 Fleet, and Spot Fleet. This innovation automated instance type selection based on user-defined attributes, significantly improving flexibility and operational efficiency for AWS customers. Extensively covered in AWS blogs, this feature is now widely adopted and critical for scaling diverse workloads. 

 

Amazon Web Services (AWS) | EC2 Global Expansion: 

I played a pivotal role in launching AWS EC2 regions globally, including high-security regions like LCK for U.S. government applications. I designed automated workflows for infrastructure setup, reducing manual labor by 50% and enhancing system reliability. These efforts supported AWS’s expansion into new markets, ensuring latency reduction and compliance with regional data residency requirements. 

 

Open Source Contributions | AWS Ecosystem Enhancements: 

I actively contributed to several open-source projects, including: 

 

Developing a Jenkins Plugin for EC2 Fleet, enabling dynamic scaling of build agents. 

 

Improving the AWS Node Termination Handler, ensuring safe EC2 Spot Instance terminations. 

 

Enhancing the amazon-ec2-instance-selector tool to provide more accurate instance recommendations. 

These contributions simplified cloud resource management, benefitting a global user base while fostering collaboration in the open-source community.