Building intelligent systems. Turning data into insights. Engineering software that works.
AI ENGINEERING ◆ DATA SCIENCE ◆ PROJECTS ◆ TECH STACK ◆ GITHUB ACTIVITY
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◈ About Me
I’m a Computer Applications student at Amity University, India, focused on AI engineering, machine learning, data science, and software development.
I work across the AI development stack—from data processing and model evaluation to LLM-powered applications, backend APIs, and automation.
I enjoy understanding how systems work, solving problems through code, and building applications that connect intelligent models with practical use cases.
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✦ Featured Work
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RAG-Powered Academic Assistant A Retrieval-Augmented Generation system that grounds language model outputs in coursework, syllabi, and technical textbooks. Features structure-aware chunking, sentence-transformer embeddings, FAISS similarity search, and Groq inference. Python RAG FAISS LangChain Groq Streamlit |
Data Science & Machine Learning An end-to-end financial intelligence pipeline combining Isolation Forest anomaly detection, NLP-based transaction classification, recurring-payment detection, and time-series forecasting for cash-flow analysis. Python Scikit-learn Pandas NumPy Time Series Plotly |
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Predictive Maintenance A machine-monitoring system that processes vibration, acoustic, and thermal sensor telemetry. Uses a RandomForest classifier to estimate equipment failure risk and a min-heap priority queue to prioritize maintenance. Python RandomForest FastAPI MongoDB Sensor Data DSA |
🍵 HotTeaPrivacy-First AI Browser & LLM Workspace A cross-platform AI workspace combining local inference through Ollama and llama.cpp with cloud LLMs. Includes DOM context extraction and a client-side AES-GCM-encrypted .tea vault. Flutter Dart Ollama Local LLMs AES-GCM Privacy |
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◈ AI Engineering
Area Technologies & Methods LLM Engineering Transformers, prompt engineering, fine-tuning, PEFT RAG & Retrieval Embeddings, semantic search, retrieval pipelines AI Agents Tool calling, workflow orchestration, execution planning Model Evaluation Benchmarking, classification metrics, error analysis Deep Learning PyTorch, neural networks, model training Computer Vision Vision-language models, image understanding
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◈ Data Science & Machine Learning
Working with data to develop models, evaluate performance, and extract useful information.
- Data Processing: NumPy, pandas, data cleaning, and feature engineering.
- Statistical Learning: Classification, regression, model selection, and evaluation.
- Forecasting: Time-dependent spending analysis and predictive modeling.
- Visualization: Exploring datasets and communicating analytical results.
- Model Evaluation: Accuracy, precision, recall, F1 score, and error analysis.
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◈ Technical Stack
AI & Machine Learning
Languages
Backend & APIs
Databases & Infrastructure
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◈ GitHub Activity
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◈ Beyond Code
First principles. Understanding a problem before choosing a solution.
Philosophy. Exploring knowledge, reasoning, reality, and the questions behind our understanding of the world.
Building. Learning by implementing ideas, examining failures, and improving systems.
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Portfolio · GitHub · X
Shomay Singh Parihar · India
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