Building intelligent, high-throughput systems at the frontier of Agentic AI and software engineering.
I engineer production AI systems, from LLM-powered autonomous pipelines to high-throughput time-series analytics backends. My work bridges predictive machine learning microservices, agentic AST bug detection engines, and enterprise identity security — published in IEEE conferences and proven in national hackathon titles.
Developing enterprise AI agentic workflows and identity security intelligence solutions. Engineering scalable automation pipelines and optimizing identity governance models for enterprise security.
Developed AI solutions for industrial automation. Built and evaluated LLM-based pipelines for text summarization, document classification, and workflow augmentation.
Architected a normalized schema of 150+ database tables. Engineered a scalable multi-tenant backend (Node.js, TimescaleDB) supporting high-volume time-series queries. Built real-time operational dashboards and analytics.
AI-Powered RFP Analysis & Clause Extraction System — automated parsing, clause classification, and requirement extraction from complex RFP documents with compliance gap evaluation.
Patient Readmission Risk Prediction System — Random Forest machine learning engine deployed as a high-throughput Flask microservice, evaluated on 101,766 clinical records.
Agentic C++ Bug Detection System — hybrid architecture pairing a curated static C++ rule engine with LLM fallback (Groq OSS-120B) for zero-day bug detection.
Available for AI Engineering, Agentic Systems, and Full-Stack development roles & research collaborations.