Lead Software Engineering Intern — Genesis Training & Consulting Services (led an 11-member engineering team, coordinated development workflows, and drove project execution)
Open-Source Maintainer — AlgoNest published on PyPI ·
pip install algonest· 14 algorithm domains · 50+ typed primitives
Software Product Creator — MediaCleaner Designed, developed, and launched a standalone desktop application distributed commercially as a Windows .exe
Certified — Meta Database Engineer Professional Certificate · 100 Days of Code Python · Mastering DSA in C/C++
Core Stack — Python · C++ · Flask · FastAPI · PostgreSQL · OpenAI API · REST · pytest
I'm a backend-focused software engineer based in Bengaluru, India — graduating B.E. Computer Science from JSS Academy of Technical Education in July 2026.
I build systems that ship: an open-source Python library on PyPI, a commercial media tool on Gumroad, and an LLM-integrated Flask platform. At Genesis, I led an 11-member engineering team delivering an AI-first recommendation platform — owning architecture, sprint planning, and engineering standards end-to-end.
My work sits at the intersection of Python backend engineering, open-source software, and AI integration. I care about clean API design, reproducible builds, and software that solves real problems without unnecessary complexity.
📍 Bengaluru, India | 🎓 B.E. CSE · 2022–2026 | 📦 Open Source Contributor | 🛒 Commercial Product Builder
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AlgoNest is a production-grade Python library for algorithms and data structures — designed with typed, iterable-first APIs and real-world engineering practices. pip install algonestWhat's inside:
This is my flagship open-source project. Built to be usable, not just demonstrable.
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Feb 2026 – May 2026 · Bengaluru, India
Led an 11-member cross-functional engineering team building an AI-first digital recommendation platform.
- Platform Architecture — designed infrastructure setup, deployment strategy, scalable repo organization, and folder structures for long-term maintainability across parallel workstreams
- AI Pipeline Ownership — directed Python-based AI pipeline architecture, REST API layer design, conversational search feature planning, and AI-driven product discovery workflows
- Engineering Standards — established Git/GitHub collaboration practices, PR review workflows, and coding standards across the full team
- SDLC Leadership — managed Spiral SDLC sprint planning, milestone coordination, and structured release cycles
Nov 2025 – Jan 2026
Contributed to C++ engineering projects involving data processing pipelines, structured data handling, and software development practices within a collaborative engineering environment.
📦 AlgoNest — Python DSA Toolkit
Problem: No lightweight, typed, installable Python library for algorithm primitives with clean APIs. Solution: Published a modular open-source library on PyPI spanning 14 domains — search, sort, graphs, dynamic programming, heaps, greedy, and more. Highlights: Typed iterable-first APIs · pytest coverage · public docs · contribution guidelines · full packaging lifecycle Outcome:
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🧹 MediaCleaner — Media Deduplication Engine
Problem: Large media libraries accumulate duplicates — existing tools require complex setup or paid subscriptions. Solution: Built a perceptual hashing engine using OpenCV for memory-efficient duplicate detection across image and video directories. Highlights: Batch processing pipelines · zero-install Outcome: Commercially released on Gumroad — a shipped product with real users. |
🧘 Smara AI — LLM-Powered Flask Platform
Problem: Digital journaling lacks personalized, context-aware insight and reflection support. Solution: Flask REST API backend integrating OpenAI API for LLM-driven insights, sentiment analysis, and AI-generated recommendations. Highlights: Context-aware prompt engineering · encrypted local storage · role-based auth · structured API design Outcome: Full-stack AI-integrated platform demonstrating end-to-end LLM application architecture. |
🎓 Placement Management System — Enterprise Campus Platform
Problem: Campus placement coordination involves fragmented manual workflows across students, recruiters, and coordinators. Solution: Normalized PostgreSQL relational schema with RBAC supporting concurrent multi-role workflows. Highlights: Real-time tracking · automated notifications · analytics dashboards · three-role access model Outcome: Centralized visibility and reduced coordinator overhead through automated workflows. |
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Core
Backend & APIs
Testing & Practices
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AI & LLM Engineering
Data & ML
Frontend
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| Certification | Issuer | Year |
|---|---|---|
| Meta Database Engineer Professional Certificate | Meta · Coursera | 2025 |
| 100 Days of Code: Python Pro Bootcamp | Udemy | 2025 |
| Mastering Data Structures & Algorithms in C/C++ | Udemy | 2024 |
- Python at depth — CPython internals, bytecode, the GIL, memory model, interpreter lifecycle, and how the runtime actually executes code
- AI Engineering — building production agentic systems, RAG pipelines, tool-use patterns, and LLM orchestration beyond basic API calls
- Scalable backend architecture — distributed systems fundamentals, system design patterns, and high-throughput API design
- Production deployment — containerization, CI/CD workflows, and engineering for reliability at scale
