I'm a final-year Artificial Intelligence student who enjoys turning ambitious ideas into dependable products. My work spans full-stack applications, LLM systems, developer infrastructure, and practical machine learning.
I care about the whole path: useful idea, thoughtful interface, reliable deployment.
| Shipping now | Deepening | Collaborating |
|---|---|---|
| Evolveus, an assessment platform my college uses to run exams from setup through LLM-assisted descriptive evaluation. | LLM evaluation, reliable agent workflows, and platform engineering with Go, Kubernetes, and Crossplane. | AI and full-stack systems where product quality and operational rigor both matter. |
Evolveus Intelligent assessment platformUsed by my college to conduct examinations. It pairs a focused assessment workflow with LLM-assisted descriptive-answer evaluation, backed by a production-ready DevSecOps foundation. |
Magic Next.js Template Composable full-stack foundationA configurable Next.js starter that makes durable application primitives selectable: auth, typed APIs, Prisma, Redis, object storage, and multi-theme UI. |
HEA QLoRA Fine-tuning Domain-tuned LLM researchA practical fine-tuning study for High Entropy Alloys research, exploring how specialist knowledge can become useful model behavior. |
Go services, Kubernetes-native delivery, Crossplane-based infrastructure, and observability that makes AI systems dependable in production. |
Application and AI
Backend and data
Platform and delivery
What I reach for
- Product engineering: TypeScript, React, Next.js, Node.js, Flutter, tRPC
- AI and data: Python, LLMs, agents, fine-tuning, evaluation systems
- Backend and services: Go, Java, Spring Boot, Django, Flask, PostgreSQL, Prisma, Redis, MongoDB, Firebase
- Platform engineering: Docker, Kubernetes, Crossplane, Ansible, Jenkins, GitHub Actions, observability
