🔭 I’m currently working on
Designing and building production-grade full-stack applications using the MERN stack, with a strong emphasis on modular and layered architecture, secure authentication & authorization (JWT), payment workflows (Razorpay), API versioning, input validation, and scalable RESTful services.
In parallel, I’m working on my B.E. final-year project, where I apply algorithmic optimization, data-driven modeling, and system-level performance analysis to solve an engineering problem through a software-first, problem-solving approach.
👯 I’m looking to collaborate on
Software engineering projects focused on real-world problem solving, including sustainability platforms, fintech systems, healthcare applications, and data-intensive web products—especially those that prioritize clean architecture, scalability, performance optimization, and maintainable codebases.
🤝 I’m looking for help with
Backend system design, API scalability patterns, database indexing & query optimization, distributed system fundamentals, and best practices for deploying, monitoring, and securing applications in cloud environments (AWS / GCP).
🌱 I’m currently learning
- Advanced SQL (window functions, execution plans, indexing strategies)
- React internals, rendering lifecycle, and performance optimization
- Backend scalability patterns (pagination, caching, rate limiting, async & event-driven processing)
- Applied machine learning concepts for optimization and decision-making problems
- Interview-oriented DSA with real-world system and backend use-case mapping
💬 Ask me about
REST API design, MongoDB & SQL schema modeling, authentication & role-based access control, payment gateway integrations, Git/GitHub workflows, debugging production issues, and translating problem statements into scalable, production-ready software solutions.
⚡ Fun fact
I enjoy decomposing complex systems into well-defined, reusable components—whether it’s backend services, data pipelines, or algorithmic problems—and explaining them with clarity and precision.
- Building an ML-driven trajectory optimization system to dynamically plan UAV flight paths with the goal of improving 5G network coverage, throughput, and latency efficiency.
- Framing the problem as a constraint-based optimization model, incorporating energy constraints, coverage guarantees, mobility dynamics, and QoS requirements.
- Designing data preprocessing pipelines, feature engineering workflows, and model evaluation metrics to support intelligent trajectory selection.
- Applying algorithmic optimization, simulation-based validation, and performance benchmarking, strengthening skills in problem decomposition, scalable computation, and system-level reasoning aligned with SDE roles.
