My journey into Machine Learning, Deep Learning, and large-scale systems began with a deep curiosity about how intelligent systems learn, adapt, and scale to solve complex real-world problems. Over time, this curiosity evolved into hands-on experience across predictive modeling, neural network architectures, computer vision systems, and production-grade AI applications. I am particularly drawn to problems where data, algorithms, and software engineering intersect, and where thoughtful system design leads to measurable, real-world impact.

I completed my Master of Science in Computer Science from Stevens Institute of Technology in December 2025, where I strengthened my foundation in data structures, distributed systems, cloud infrastructure, and AI/ML systems. My graduate studies deepened my system-level understanding and complemented my prior industry experience, reinforcing my ability to reason about scalability, reliability, and performance in distributed environments.

Before graduate school, I worked at Infosys as a Senior Software Engineer (Senior System Associate), contributing to large-scale advertising platforms operating under strict performance and reliability requirements. I engineered high-throughput backend services, optimized Kubernetes-based microservices, improved concurrency models, and diagnosed complex production issues across compute, network, and storage layers. These experiences shaped my engineering mindset and instilled a strong appreciation for building systems that are observable, fault-tolerant, and scalable by design.

As my experience matured, my focus naturally expanded into MLOps and production ML systems. I became increasingly interested in the operational lifecycle of machine learning how models are deployed, monitored, versioned, and maintained in real-world environments. Rather than stopping at model performance, I prioritize latency, availability, deployment safety, and long-term maintainability, ensuring ML systems remain reliable under real-world constraints.

What distinguishes my approach is a strong emphasis on first-principles understanding. I do not stop at making systems work. I seek to understand why they work, how they fail, and how they can be improved. This mindset guides how I evaluate trade-offs, design architectures, and build solutions that are robust, scalable, and production-ready.

I view learning as a continuous and iterative process. Every project, system failure, and technical challenge adds depth to my perspective, refining my intuition and strengthening my ability to translate complex ideas into well-engineered solutions. This philosophy continues to guide my work as I build intelligent systems with lasting, real-world impact.