AI/ML Engineer specializing in LLM applications, RAG pipelines, agentic AI, and identity & risk systems.
I design and ship production-grade ML systems, not prototypes.
Brain warm-up for ML students • Linear Algebra Fundamentals
Master vector operations step-by-step with interactive visuals
Choose a demo mode to start your vector journey
I'm an AI/ML engineer with a background in backend systems, identity security, and machine-learning-driven authentication. I've worked on device-trust pipelines, MFA orchestration, and risk-scoring engines at Acceptto and SecureAuth, and I now build end-to-end AI applications through independent engineering work.
My focus is on LLM-powered systems, retrieval architectures, and agentic workflows that are reliable, grounded, and ready for production. I enjoy solving problems where ML, system design, and product engineering intersect.
Classical ML, deep learning, embeddings, evaluation
Multi-phase pipelines, RAG, grounding, tool-calling
Structured reasoning, action orchestration, safety layers
FastAPI, microservices, identity flows, secure APIs
Device trust, anomaly detection, risk scoring
CI/CD, monitoring, model lifecycle, observability
Real-time AI co-pilot desktop app for live conversations and job interviews. Captures system audio, transcribes locally with Whisper and RNNoise, and fires parallel LLM guidance threads in a stealth overlay that is invisible to screen-share capture. RAG over session context via sqlite-vec keeps answers grounded in your own experience.
AI-powered job-search platform. Build a persistent master resume by voice (Story Mode + coached interview) or upload, tailor it to any job description through a four-phase agent pipeline, generate cover letters, check job-fit scores, search for matching listings, and track every application — all in one place.
End-to-end fraud detection pipeline for credit card transactions. Covers EDA with timezone-aware feature engineering, scalable PySpark preprocessing, supervised ML, deep learning (MLP/ResNet/LSTM), transformer-based models (FT-Transformer), hybrid ensemble stacking, and a real-time Streamlit inference app with a two-stage risk-tiering system (Auto-Block / Review / Cleared).
A comprehensive ML solution predicting customer term deposit subscriptions using 8+ algorithms with hyperparameter tuning, achieving 60% F1-Score improvement over baseline models.
Production-ready machine learning solution for predicting customer churn with 84.77% ROC-AUC. Features 16 optimized models, SHAP interpretability, Optuna hyperparameter tuning, and comprehensive business insights.
Production-ready Flask web application providing comprehensive REST API and interactive UI for the Movie Agent Service. Features intelligent movie discovery through natural language queries, real-time chat interface with conversation history, movie poster image analysis using computer vision, interactive movie quizzes, and formatted statistics display. Implements encrypted API key storage with local configuration management, session-based memory isolation for multi-user support, and automatic query classification for seamless routing.
Production-grade core library for AI-powered movie discovery and analysis. Implements RAG-based semantic search using FAISS vector store, tool-calling agent architecture with LangChain (chosen over ReAct for reliability), computer vision poster analysis with BLIP model, interactive quiz generation, and session-based memory management. Built with Python following SOLID principles, dependency injection, and factory patterns for maintainability and extensibility.
Advanced regression pipeline predicting satellite signal quality from weather conditions using multiple ML algorithms and real-time API data.
Machine learning project predicting passenger survival using ensemble methods and feature engineering techniques.
Learn the fundamentals of Natural Language Processing with interactive examples and hands-on exercises.
Master Naive Bayes classification with interactive weather prediction demo and comprehensive mathematical explanations.
Complete hands-on course with Python implementations and real-world examples covering introduction, regression, and classification.
When I first started learning machine learning, gradient descent was one of those concepts that seemed intimidating. Everyone talked about it like it was this magical thing that made models work, but nobody really explained what it actually does.
Let's discuss how machine learning and data science can drive your next project forward.
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