
Kothe Viswanath
Software Engineer Intern at Syracuse University
About
I'm Kothe Viswanath, currently a Software Engineer Intern at Syracuse University focusing on AI Infrastructure and Distributed Systems. My career has taken me from designing flight software at ISRO to building data science solutions at Nuviso Networks, and I'm now completing my MS in Computer Engineering. I'm deeply passionate about the intersection of MLOps and system internals—I love taking a 'deep dive' into how things work 'under the hood,' whether that's building custom container runtimes or orchestrating multi-agent AI workflows. I'm a firm believer in engineering rigor and avoiding early over-engineering. I enjoy mentoring others in cloud deployment and sharing my findings through technical writing. I’m looking to connect with fellow engineers and teams who are building the next generation of reliable, mission-critical AI systems.
Networking
What I can offer
- ›Mentorship in cloud deployment and containerization
- ›Deep-dive technical insights into MLOps and Go
- ›Expertise in building reliable AI systems and infrastructure
Looking for
- ›expanding my professional network
- ›exploring mutual opportunities in AI infrastructure and ML teams
Best fit for
Current Interests
Background
Career
Began as a System Engineer at SSPACE, IIST, followed by a role as a Software/Data Scientist at Nuviso Networks. Served as a Scientist/Engineer at ISRO before transitioning to a Software Engineer Intern role at Syracuse University while pursuing graduate studies.
Education
Master of Science (MS) in Computer Engineering, Syracuse University (Expected 2025); Bachelor of Technology (BTech) in Electrical Engineering, Indian Institute of Space Science and Technology (2021).
Achievements
- ›Achieved over 87% accuracy in MARL research for SMAC-Starcraft II
- ›Designed flight software for AAReST project reducing operational costs
- ›Built a custom container runtime and manager from scratch
- ›Developed 'JarVish' LLM-powered personal agent using LangGraph
Opinions
- A model is not 'done' after training; it must be served, monitored, and adapted continuously.
- MVP ≠ Production ≠ Enterprise; over-engineering early kills iteration.
- The future of tech requires both generalists and specialists.
- Interview processes should prioritize problem-solving over specific technical skills.