Building production-ready AI systems and scalable web applications.

Computer Science undergraduate focused on building end-to-end AI-powered and full-stack systems.
I design, implement, and deploy scalable applications with strong emphasis on clean architecture, modular code, and containerized environments.

Core Focus Areas:

• Retrieval-Augmented Generation (RAG)
• Generative AI Systems
• Scalable Backend Architectures
• System Design & Deployment

Problem: Context-aware AI retrieval across diverse document formats.
Built: End-to-end RAG system with ingestion, embeddings, vector search, and response orchestration.
Tech: Python, LangChain, FAISS, LLM APIs, Docker
Highlights:

• Processed 1,000+ documents for semantic retrieval
• Reduced retrieval latency by ~30% via optimized vector indexing
• Modular ingestion architecture supporting extensibility
• Containerized deployment for reproducibility

🔗 Repository: https://github.com/himaenshuu/Multi_modal_rag-application

Problem: Secure and scalable document management system.
Built: Full-stack storage platform with authentication and structured APIs.
Tech: Next.js, Node.js, MongoDB, TypeScript
Highlights:

• Implemented JWT-based authentication system
• Handled 10,000+ file metadata records in MongoDB
• Reduced API response time by ~25% through query optimization
• Structured production-ready project architecture

🔗 Repository: https://github.com/himaenshuu/smartVault

Problem: Lack of contextual product assistance before purchasing.
Built: AI-powered product Q&A and recommendation assistant integrating external APIs.
Tech: TypeScript, Next.js, External APIs
Highlights:

• Integrated 500+ product records via API ingestion
• Designed structured recommendation logic pipeline
• Improved response clarity using contextual filtering
• Implemented automated email-based purchase workflow

🔗 Repository: https://github.com/himaenshuu/ShopSense

Problem: Limited accessible analytics tools for social media insights.
Built: Full-stack analytics dashboard generating structured insights.
Tech: TypeScript, Next.js
Highlights:

• Analyzed 5,000+ profile engagement data points
• Built reusable analytics visualization components
• Optimized state management for faster UI rendering
• Designed scalable dashboard architecture

🔗 Repository: https://github.com/himaenshuu/InstaLens

Problem: Raw datasets require transformation before analytics usage.
Built: Python-based ETL pipeline for cleaning and loading structured data into MongoDB.
Tech: Python, MongoDB
Highlights:

• Processed 50,000+ raw records
• Improved data consistency by implementing schema validation
• Reduced manual preprocessing time by ~40%
• Designed reusable transformation modules

🔗 Repository: https://github.com/himaenshuu/etl_mongo_pipeline

Problem: Unstructured ecommerce API responses are difficult to use directly for analytics or modeling.
Built: Data ingestion and transformation workflow converting API responses into structured datasets.
Tech: Python, Jupyter Notebook
Highlights:

• Product feature extraction
• Data normalization and cleaning
• Analytics-ready structured output
• Foundation for recommendation systems

🔗 Repository: https://github.com/himaenshuu/Amazon-Product-Intelligence-Data-Builder

AI & ML: TensorFlow • LangChain • RAG • LLM APIs
Backend: Node.js • Python • REST APIs
Frontend: Next.js • React • TypeScript
Databases: MongoDB • MySQL
DevOps: Docker • Git • GitHub • Postman

• Write modular, maintainable code
• Design scalable architectures
• Prefer reproducible, containerized environments
• Optimize where measurable
• Document clearly for collaboration

Strong engineering balances clarity, performance, and real-world usability.

Open to:

• Software Engineering Internships
• AI / ML Engineering Roles
• Backend & Full-Stack Development Roles

Experienced in production-style deployments and containerized AI systems.