Software Engineering & Computer Science student (2025–2029) focused on high-performance Full Stack development. I work with an engineering vision, combining modern interfaces with robust backends, guided by Clean Architecture, Domain-Driven Design (DDD), and SOLID.
My main stack is the TypeScript ecosystem (Node.js, React, Next.js, NestJS), with Python and Go used strategically for automation, data workflows, and concurrency-critical services. I am actively pursuing internship opportunities where I can apply modern web development, cloud-native delivery, and applied AI/ML to real-world systems.
const profile = {
title: "Software Engineering Student",
specialization: "Full Stack Development",
stack:
"TypeScript (Node.js, React, Next.js, NestJS) | Python & Go | Cloud, DevOps, Databases & AI/ML",
education: {
field: "Software Engineering & Computer Science",
period: "2025–2029",
},
coreFocus: [
"Scalable and High-Performance Web Systems",
"Cloud-Native Architecture and Delivery",
"AI/ML Integration in Production Systems",
],
engineeringPrinciples: [
"Clean Architecture",
"Domain-Driven Design (DDD)",
"SOLID Principles",
],
};
- Full Stack delivery with production-grade ergonomics: performance, security, and maintainability.
- API design and backend systems: REST/GraphQL, microservices patterns, messaging, and WebSockets.
- Cloud & DevOps: containerization, CI/CD pipelines, and cloud deployment workflows.
- Data: relational and NoSQL modeling, query optimization, and caching strategies.
- AI/ML: model training and integration patterns for intelligent features in web systems.
Full Stack Systems React, Next.js, Node.js, NestJS |
Cloud & DevOps Docker, Kubernetes, CI/CD, AWS |
AI/ML Integration Python, Deep Learning, Kaggle |
| Domain | Primary focus | Notes |
|---|---|---|
| Frontend | React, Next.js (SSR/ISR), TypeScript | Accessible UI, performance, web fundamentals |
| Backend | Node.js, NestJS, REST/GraphQL | Microservices patterns, WebSockets, security |
| Cloud & DevOps | Docker, Kubernetes, CI/CD, AWS | Cloud delivery mindset; familiarity with Azure/GCP |
| Data | PostgreSQL, MySQL, MongoDB, Redis | Efficient modeling, indexing, query optimization |
| AI/ML | PyTorch, TensorFlow, Hugging Face | Kaggle-driven experimentation and integration |
I optimize for reliability and long-term maintainability by applying proven principles and patterns:
- Clean Architecture: explicit boundaries, testable business rules, infrastructure adapters.
- DDD: ubiquitous language, domain modeling, and clear separation of concerns.
- SOLID: small units with cohesive responsibilities and stable contracts.
- System design: decomposition, backpressure, caching, and failure-aware design.
flowchart LR
U[Users] --> E[Edge / CDN]
E --> FE[Frontend: Next.js]
FE --> API[API Gateway]
API --> S1[Service A: NestJS / Node.js]
API --> S2[Service B: Go / Python]
S1 --> DB1[(PostgreSQL / MySQL)]
S1 --> C[(Redis Cache)]
S2 --> DB2[(MongoDB)]
S2 --> ML[ML Inference / Models]
CI[CI/CD] --> R[Container Registry]
R --> K8s[Kubernetes]
K8s --> S1
K8s --> S2
Engineering checklist (quality, security, performance)
- API design: versioning strategy, validation, rate limiting, and observability.
- Performance: profiling, caching, indexing, and pragmatic trade-offs.
- Security: least privilege, secrets hygiene, OWASP basics, and secure defaults.
- Delivery: CI/CD automation, containerized runtimes, reproducible environments.
Machine Learning & Data Science competitor on Kaggle. I use competitions to benchmark models, refine feature engineering, and stress-test applied ML workflows.
Technical focus
- Training Deep Learning models with PyTorch/TensorFlow.
- Mathematical foundations for optimization and regularization (linear algebra, multivariable calculus, probability/statistics).
- Feature engineering and large-scale data processing.
- Computer Vision and NLP for unstructured problems.
- Continuous study of SOTA architectures and modern training techniques.
| Position | Focus areas | Goal |
|---|---|---|
| Internship / Junior | Full Stack, Backend, Cloud | Apply engineering fundamentals in production contexts |
| Collaboration | High-impact systems | Deliver scalable, maintainable code |
Building scalable systems with clean interfaces, robust backends, and measurable outcomes.