Computer Science & IoT Undergraduate
PRIYANSHU SHEKHAR
Aspiring AI Engineer & Software Developer
Focused on Artificial Intelligence, Software Engineering, and building real-world applications. Passionate about translating complex problems into elegant technical solutions.
Systematic
Problem
Solving.
I am a Computer Science and IoT undergraduate with a strong foundation in software development and an active focus on artificial intelligence. I enjoy building real-world applications, solving complex problems, and continuously expanding my technical knowledge across the full stack.
Core Focus
- AI
- Software Engineering
- Full Stack
Currently Learning
- AI Engineering
- System Design
- Advanced Development
Engineering Work
Problem
Financial institutions require extremely low-latency, interpretable fraud detection systems that can scale and explain their decisions to analysts.
Solution
Engineered a full-stack, real-time fraud detection pipeline integrating an XGBoost ML model with a FastAPI backend and a Next.js WebSocket dashboard.
Engineering Highlights
- Engineered a real-time fraud detection API using FastAPI, predicting transaction risk scores with sub-second latency using an optimized XGBoost model.
- Implemented a live WebSocket-based Next.js dashboard for analysts to monitor high-risk transactions, visualize risk distributions, and receive instant anomaly alerts.
- Built an automated MLOps pipeline covering synthetic data generation, feature engineering, model training, and SHAP-based explainability for transparent AI decisions.
- Integrated LLM capabilities for automated risk report generation and contextual analysis of suspicious transaction patterns.
- Architected a scalable deployment setup utilizing Docker, Nginx, and GitHub Actions for continuous integration and delivery.
Tech Stack
Problem
Experimenting with multiple ML models and comparing their explainability metrics usually requires extensive custom scripting.
Solution
Built a unified visualization lab that allows rapid experimentation, evaluation, and visualization of 10+ machine learning workflows in a single intuitive interface.
Engineering Highlights
- Built an interactive Streamlit application supporting preprocessing, model training, evaluation, and visualization.
- Integrated 8+ analytical visualizations including confusion matrices, ROC/PR curves, SHAP analysis, and feature importance.
- Designed an intuitive interface for comparing multiple machine learning models, enabling faster experimentation and model selection.
Tech Stack
Problem
Traversing large directory structures and filtering files often consumes excessive memory and time when done inefficiently.
Solution
Engineered a low-memory, high-performance file operations library with streaming I/O and .gitignore-style filtering.
Engineering Highlights
- Engineered a library using C++17 and Python to scan, search, hash, and index thousands of files with low memory overhead.
- Optimized file processing through streaming I/O and ".gitignore"-style filtering, preventing entire files from being loaded into memory.
- Designed a modular, cross-platform architecture with automated testing and robust exception handling.
Tech Stack
Problem
Text-based files require efficient compression to reduce storage without risking data loss.
Solution
Developed a custom binary file format and compression pipeline based on Huffman Coding that achieves 100% data integrity.
Engineering Highlights
- Developed a lossless file compression utility capable of compressing and restoring files with 100% data integrity.
- Implemented a custom binary file format and pipeline that achieves storage savings on text-based files.
- Built a modular C++ application with CMake-based build automation and test cases ensuring portability.
Tech Stack
By The Numbers
A snapshot of my coding consistency, problem-solving progress, and technical activity.
Technical Arsenal
programming
frontend
backend
databases
AI / ML
tools
Experience
AI Engineer Intern
Apr 2026 - May 2026- Developed a Retrieval-Augmented Generation (RAG) application using Python, LangChain, FAISS, and Hugging Face to deliver context-aware breast cancer predictions with evidence-backed explanations.
- Built an end-to-end AI pipeline spanning data preprocessing, embedding generation, vector indexing, semantic retrieval, and LLM inference to improve the relevance and interpretability of model outputs.
- Implemented FastAPI backend services integrated with a Streamlit interface, enabling seamless interaction with the RAG pipeline through a user-friendly web application.
- Enhanced response quality through prompt engineering and retrieval optimization, while collaborating with the team using Git/GitHub and code review best practices.
Education
B.Tech in Computer Science and Engineering (IoT)
Class 12 (Intermediate)
Class 10
Have an Idea?
Let's Build Something Interesting.
Always open to discussing new projects, technical challenges, and opportunities in AI & Software Engineering.