I'm a B.Tech Computer Science & Engineering student at Lovely Professional University, building toward backend engineering, full-stack development, and applied AI. I like the layer underneath a feature: the data model, the API boundary, the rule that prevents an invalid state, and the tests that prove the behaviour.
I'm studying Computer Science & Engineering at Lovely Professional University (2024–2028). My interests are backend systems, full-stack products, databases, problem solving, and practical AI integrations.
I enjoy taking a project beyond a first demo: making the rules explicit, validating inputs, handling edge cases, testing behaviour, and documenting decisions. The projects below are at different stages, so I distinguish implemented functionality from plans and work in progress.
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Backend & API engineering REST APIs, authentication and authorization, data modelling, validation, and maintainable service boundaries. Node.js · Express · Python · API design |
Full-stack product development Connecting user workflows to backend services and building interfaces for customers and administrators. JavaScript · HTML · CSS · responsive interfaces |
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Eligibility, matching & automation Representing opportunity data consistently, applying explicit eligibility rules, and making recommendations easier to inspect. EligiCore · data modelling · testing |
Privacy-aware automation & AI Exploring safer automation boundaries and practical AI integrations without treating model output as unquestionable truth. PratiBimb · AI-provider boundaries · safety design |
Student opportunity eligibility and assisted-application platform.
EligiCore aims to help students understand which opportunities fit their profiles and move through application preparation with a human checkpoint before final submission. Its design separates resume/profile processing, job ingestion, eligibility, ranking, and application preparation.
- Structured inputs: parse resume information into a reusable candidate profile.
- Explicit decisions: keep eligibility and recommendation outcomes understandable and testable.
- Ingestion boundaries: normalize source records and handle duplicates consistently.
- Human control: do not bypass CAPTCHAs or automatically click final submit on third-party sites.
- Validation-first development: use automated tests to check rules and edge cases as the project evolves.
Stack: Python · API development · data modelling · AI-provider integration · automated tests
Repository: Lakshya172/eligicore ↗
Privacy-aware browser-agent safety layer · project design and early-stage work.
PratiBimb explores how browser automation can handle sensitive information more carefully. The design direction includes typed placeholders for personally identifiable information, explicit verification boundaries, restricted outbound requests, and a memory-only vault concept.
- Privacy by design: avoid casually exposing sensitive values to every automation step.
- Fail-closed verification: stop when a required safety check cannot be satisfied.
- Bounded actions: treat browser actions and outbound requests as policy-controlled operations.
- Honest project status: this should be described as an early-stage design until implementation, tests, and measurements are present in the repository.
Technology direction: TypeScript · browser automation concepts · ONNX Runtime Web · Playwright · Vitest
Repository: [Add the exact PratiBimb repository URL before publishing.]
Full-stack e-commerce platform · Rinky Jewellers, Kanpur.
A commerce application for a real jewellery business, with a customer-facing storefront and an administration workflow for managing products.
- REST backend built with Node.js and Express.
- MongoDB Atlas database with Mongoose.
- Authentication and role-based authorization.
- Product catalogue and admin product management.
- Customer wishlist and order-related workflows.
- Transactional email verification using Brevo.
- Responsive frontend built with HTML, CSS, and vanilla JavaScript.
- Security middleware and request validation.
Stack: Node.js · Express.js · MongoDB Atlas · Mongoose · HTML · CSS · JavaScript · Brevo · Render
Live: rinkyjewellers.in ↗ · Repository: rinky-commerce-engine ↗
A computer-vision project for webcam-based face recognition and attendance recording.
- Webcam-based face detection and recognition.
- KNN-based classification.
- Automated attendance recording and CSV storage.
- Interactive interface for running the workflow.
Stack: Python · OpenCV · KNN · Streamlit · CSV
Repository: Attendence-System ↗
| Layer | Technologies |
|---|---|
| Languages | Python · JavaScript · Java · C · C++ |
| Backend & APIs | Node.js · Express.js · REST APIs |
| Frontend | HTML · CSS · Vanilla JavaScript |
| Databases | MongoDB · MongoDB Atlas · SQL |
| AI / data | AI-provider integration · NLP fundamentals · data analysis |
| Tools & delivery | Git · GitHub · VS Code · Render |
| Foundations | Data structures & algorithms · DBMS · OOP · software engineering |
A feature is more than the screen where it appears. I’m learning to reason across the full path: the user action, the application rules, the persistence layer, and the integrations that connect the system to the outside world.
A feature isn't finished just because the happy path works.
A rule should be testable.
A failure state should be deliberate.
Sensitive data should have boundaries.
A project claim should match what the code can demonstrate.
- Make rules explicit. Business logic should be understandable and testable.
- Treat edge cases as design work. Missing data, duplicate records, and invalid states need deliberate behaviour.
- Separate responsibilities. Keep API, persistence, and application logic from becoming one tangled layer.
- Test behaviour, not just execution. A successful response is not enough if the resulting state is wrong.
- Describe work honestly. Distinguish shipped features from experiments, designs, and planned work.
- Backend engineering: service structure, REST API design, validation, and authorization.
- Data structures & algorithms: consistent problem solving and complexity analysis.
- EligiCore: improving ingestion, eligibility, matching, and test coverage.
- Applied AI: understanding useful integrations and evaluating output responsibly.
- Open source: preparing to make meaningful contributions through code, tests, and documentation.
- System design: learning to explain architecture decisions and their trade-offs.
B.Tech — Computer Science & Engineering
Lovely Professional University · 2024–2028
- Data Science & Analytics — HP LIFE
- Effective Communication Skills — Skillera
- Advanced Backend Development — Node.js, Express.js, MongoDB · Grade A, Certificate of Merit · LPU
If you're building practical software, working on backend systems, or interested in thoughtful open-source collaboration, feel free to connect.
