I am a Graduate Computer Science student at UC Davis, graduating in December 2026, focused on building scalable software and data systems at the intersection of full-stack development and applied AI. This summer, I will be joining Cloudflare as a Software Engineer Intern in Austin, Texas, working with the UI Platform Team on the Cloudflare Dashboard, a large-scale React/TypeScript application used by millions of developers and companies worldwide to manage DNS, security, Workers and other core services. I previously earned my B.Tech in Information Technology with Honors in Artificial Intelligence from K.J. Somaiya College of Engineering, graduating Summa Cum Laude with a GPA of 3.91. This is where my interest in combining strong software engineering fundamentals with applied AI first took shape. My path into technology has been driven by curiosity and reinforced through measurable impact. As a Software Engineering Intern at Talent Questor, I built fault-tolerant Stripe payment pipelines, engineered seamless frontend-backend integrations and developed secure Node/Next.js microservices with Firebase, cutting API latency by 35 percent and raising payment success rates to 85 percent. I also built large-scale data pipelines for efficient processing across distributed systems. At DeepCytes Cyber Labs, I spent two years working at the intersection of software engineering and cybersecurity, automating a real-time CVE and CWE tracking system, building a CLI-based red teaming and OSINT toolkit, and uncovering 37 new attack vectors. I am currently a Graduate Research Assistant in the Visualization and Intelligence Augmentation Lab, supported by two GSR Fellowships totaling $38,000. My primary research involves CATF, a unified time series forecasting framework submitted to ACM SIGKDD 2026, built on a manager-worker architecture using attention mechanisms and optimal transport, reducing MSE loss by up to 49.3 percent across benchmarks. In parallel, I contribute to a federally funded wildlife conservation project where I engineered a scalable gunshot detection pipeline processing over 4,000 hours of field audio, achieving 94 percent accuracy at 81.5 percent recall while reducing false positives by 70 percent. At the UC Berkeley AI Hackathon, I architected llmao.ai, a stateful agentic LLM platform that semantically analyzes codebases to generate implementation documentation and API references, supporting repository-aware chat and serverless RAG pipelines built with LangChain. I have published six or more peer-reviewed papers in IEEE and Springer venues. One received a Best Paper Award at IEEE ICEEICT 2023 for knee osteoarthritis detection using DenseNet-based transfer learning. I also attended Grace Hopper Celebration 2025. My technical toolkit includes Python, JavaScript/TypeScript, React, Node.js, PyTorch, SQL, Docker, AWS, GCP, Kafka, the ELK Stack and agentic AI systems. I currently serve as a Teaching Assistant for ECS 130 at UC Davis. What drives me is constantly asking how something can be faster, smarter and more reliable, whether that means improving financial infrastructure, protecting wildlife or advancing scientific computation. I am looking for 2026 New Grad positions in Software Engineering, Machine Learning Engineering, Data Engineering or AI roles where I can bring research depth and production engineering experience to meaningful problems at scale.

If you are building something ambitious, I would love to connect. Email: hnimonkar@gmail.com