Sreeharsh Namani

WORK HISTORY

EDUCATION

ABOUT SREEHARSH NAMANI

I’m a Software Engineer and Machine Learning enthusiast passionate about building scalable systems, intelligent automation, and data-driven products that create real-world impact. At Rakuten (Tokyo), I architected Spark-based data pipelines processing over 2TB of data per hour, and developed real-time analytics dashboards that enhanced customer experience and executive decision-making. My work in distributed computing and automation improved data processing latency by 40% and streamlined KPI reporting across global operations. During my internship at Arista Networks (Austin), I contributed to distributed systems in C++ and Python, focusing on performance optimization, debugging, and reliability within large-scale production environments. Currently pursuing my MS in Computer Science at NYU, I’m deepening my expertise in machine learning, optimization algorithms, and system design while working as a Research and Teaching Assistant. My research on Federated Knowledge Distillation for Privacy-Preserving Model Aggregation was published at ICML 2025 (Vancouver) and received the Best Paper Runner-Up Award at the Fed-KDD 2025 Workshop (Toronto), recognizing innovation in decentralized learning systems. I thrive in fast-paced, global teams leveraging Python, C++, Java, Spark, AWS, Docker, and REST APIs to build robust and scalable software solutions. I bring a mix of hands-on technical depth, cross-functional collaboration, and a growth mindset to every challenge. I’m actively seeking Software Engineering opportunities for 2026, where I can contribute to developing scalable software systems, AI/ML platforms, and data-driven products that make a global impact.