Building intelligent systems using Machine Learning, Generative AI, and Retrieval-Augmented architectures.
AI engineering student focused on building production-oriented intelligent systems and applied ML architectures. Focus Areas:
- Machine Learning Engineering
- Retrieval-Augmented Generation (RAG)
- AI Agents
- Deep Learning
- End-to-end AI systems
- Build systems, not scripts
- Learn by building real-world projects
- Data-driven iteration
- Research mindset with practical execution
🧪 Selected flagship systems demonstrating applied AI engineering exploration.
These projects represent my strongest exploration toward becoming an AI engineer — focusing on real-world system design, applied machine learning, and modern AI architectures.
A machine learning system designed to forecast geomagnetic storm activity using historical solar datasets and time-series modelling.
🚀 Engineering Signals:
- Scientific dataset handling and preprocessing
- Advanced time-series feature engineering
- Ensemble model experimentation and evaluation
- Research-oriented ML workflow
🧠 Why it matters:
- Applies AI to real scientific prediction problems
- Demonstrates experimental rigor and model evaluation mindset
- Shows ability to translate research ideas into engineering pipelines
An AI-powered Retrieval-Augmented Generation system designed to answer contextual queries from distributed internal documentation.
🚀 Engineering Signals:
- Semantic vector search and embedding retrieval
- RAG pipeline architecture design
- Multi-step AI agent workflow experimentation
- Context-aware generation pipelines
🧠 Why it matters:
- Demonstrates modern AI engineering patterns
- Simulates enterprise-scale knowledge systems
- Shows applied understanding of LLM-driven architectures
Problem: Space weather forecasting requires accurate prediction of geomagnetic disturbances to reduce risks to satellites, communication systems, and infrastructure.
Solution: Designed an ML-based prediction engine using historical solar activity datasets and time-series modeling techniques to forecast geomagnetic storm events.
Highlights:
- Advanced time-series feature engineering
- Ensemble model experimentation and evaluation
- Data preprocessing pipelines for scientific datasets
- Performance benchmarking and validation workflows
- Research-oriented ML experimentation
Tech Stack:
Python, Scikit-learn, Pandas, NumPy, Time-series ML
Impact:
- Demonstrated high AUC prediction performance
- Applied ML to real-world scientific problem domain
- Showcases ability to work with complex research datasets
Problem: Organizations struggle with fragmented internal knowledge spread across multiple documentation platforms, causing productivity loss.
Solution: Built an AI-powered Retrieval-Augmented Generation (RAG) assistant that indexes internal documents and answers contextual queries using semantic search.
Highlights:
- Semantic vector search pipelines
- Embedding-based document retrieval
- Multi-step AI agent workflow design
- Context-aware response generation
- Experimentation with enterprise-scale knowledge systems
Tech Stack:
Python, LLM APIs, Vector embeddings, RAG architecture, Streamlit
Impact:
- Enables context-aware enterprise Q&A
- Demonstrates modern AI system architecture skills
- Real-world enterprise AI workflow simulation
Problem: Biological datasets are high-dimensional and require computational analysis to extract meaningful insights.
Solution: Performed transcriptomic data exploration and applied machine learning techniques to analyze gene expression patterns.
Highlights:
- Data cleaning and preprocessing for biological datasets
- Exploratory data analysis (EDA)
- Machine learning experimentation
- Feature interpretation and visualization
Tech Stack:
Python, Pandas, Bioinformatics datasets, ML experimentation
Impact:
- Applied AI techniques to biomedical data
- Demonstrates cross-domain analytical capability
Problem: Mental health risk detection using behavioral and survey data requires predictive modeling approaches.
Solution: Developed classification models to predict mental health outcomes based on dataset features.
Highlights:
- Data preprocessing and feature engineering
- Multiple classification model comparisons
- Model evaluation and performance analysis
- ML pipeline design
Tech Stack:
Python, Scikit-learn, Data preprocessing pipelines
Impact:
- Demonstrates practical ML workflow understanding
- Shows applied problem-solving using AI models
Problem: Indian street food vendors struggle with fragmented supplier networks and inconsistent raw material pricing.
Solution: Designed a web-based platform connecting vendors with verified suppliers including AI-driven matchmaking for group orders.
Highlights:
- Real-time pricing dashboard concept
- Vendor matchmaking algorithm design
- Supplier rating and review system
- Delivery ETA tracking logic
- Practical problem-focused web architecture
Tech Stack:
Web Development Stack, AI Matching Logic, UI/UX System Design
Impact:
- Real-world problem-solving focused on local economic needs
- Demonstrates product-thinking mindset
Problem: Delayed emergency response increases risk during road accidents.
Solution: Developed an automated alert system that detects accidents and triggers emergency notifications.
Highlights:
- Sensor/data-driven detection logic
- Automated alert workflow design
- Safety-focused system architecture
Tech Stack:
Python / Embedded Logic (adapt based on implementation)
Impact:
- Safety-oriented AI application
- Demonstrates applied engineering for real-world scenarios
Machine Learning • Deep Learning • NLP • Transformers • RAG • AI Agents
- Becoming industry-ready AI Engineer
- Production ML pipelines
- Advanced RAG experimentation
⭐ Building toward becoming an AI engineer through real-world projects.