About
AI engineer, software developer, and lifelong learner.
Building intelligent systems that solve real problems.
I'm Nalgonda Lokesh, an AI Engineer and Software Developer interested in building intelligent systems that go beyond prototypes.
My work focuses on Agentic AI, Retrieval-Augmented Generation (RAG), and Applied Machine Learning. I enjoy designing multi-agent workflows, building knowledge-intensive applications, and exploring how AI can solve problems across domains such as healthcare, legal research, and semiconductor engineering.
Through my internship, engineering projects, and hackathons, I've explored the journey from an idea to a working system—from designing architectures and experimenting with models to building APIs and developing usable applications.
I believe great engineering is about more than making something work. It's about understanding the problem, making thoughtful design decisions, evaluating results honestly, and building systems that people can actually use.
Currently, I'm exploring the intersection of AI agents, efficient LLM systems, and intelligent engineering tools.
My goal is simple: build useful technology, keep learning, and turn ambitious ideas into real-world impact.
Engineering Philosophy
I believe in building systems that are not only technically sophisticated but also practical and maintainable. Good engineering is about finding the right balance between innovation and reliability. I prioritize clean code, thorough testing, and continuous learning in every project I undertake.
Interests & Focus Areas
Agentic AI
Building autonomous agents that can reason, plan, and execute complex tasks.
Retrieval-Augmented Generation
Developing systems that combine LLMs with external knowledge bases for accurate, citation-backed responses.
Applied Machine Learning
Applying ML techniques to solve practical problems across various domains.
Software Engineering
Building robust, scalable software systems with modern best practices.
Achievements
Milestones built through consistency, competition, and open-source engineering.
Five-Time Hackathon Winner
Won five hackathons across AI, software engineering, and innovation, including four first-place finishes and a runner-up finish.
- • 2nd Place — CBIT Sudhee Hackathon 2024
- • 1st Place — SVIT Udbhav Data Science and ML Hackathon 2025
- • 1st Place — Sreya's Hackattack 2025
- • 1st Place — Vignan's VHACK Hackathon 2026
- • 1st Place — MVSR AETHRONIX Hackathon 2026
COSC HackWeek 2026
Rank 18 · Top 2.5% · 750+ Participants
Secured Rank 18 with 6,391 points during the seven-day COSC HackWeek 2026, organized by the CBIT Open Source Community.
NGIT Project Expo 2026
2nd Place · Nutraceutical Domain
Achieved 2nd place in the Neil Gogte Institute of Technology Project Expo 2026 for a project in the nutraceutical domain.
COSC HackWeek 2025
Top 30 · Top 2.5% · 1,200+ Participants
Finished among the top 30 contestants in COSC HackWeek 2025, demonstrating commitment to open-source development and competitive problem-solving.
GeeksforGeeks Institute Rank #1
Achieved Rank #1 on the GeeksforGeeks coding leaderboard at Neil Gogte Institute of Technology, reflecting consistent practice in data structures, algorithms, and problem-solving.
500+ DSA Problems Solved
Solved over 500 data structures and algorithms problems across coding platforms, strengthening algorithmic thinking and problem-solving skills.
Research Publication
Contextual Biomedical Language Models for Imbalance-Aware Drug-Food Interaction Classification
Proposed a framework combining BioBERT with Focal Loss optimization and class-weighting strategies for imbalanced drug-food interaction classification. Results: 85.3% accuracy and macro-F1 score of 0.77.
View Paper on Zenodo →