AI consultant helping organizations adopt artificial intelligence responsibly, efficiently, and profitably.
I'm a senior at NJIT graduating in May 2026, passionate about the intersection of business strategy and emerging AI technology. I help organizations navigate the AI landscape with practical, results-driven approaches — demystifying the technology, building internal capabilities, and delivering measurable outcomes. My approach is simple: understand your business first, then apply AI where it creates real value.
Comprehensive reviews of existing AI systems to identify bias and ensure compliance with industry standards.
Tailored strategies for integrating AI into business operations to maximize efficiency and ROI.
Development of bespoke AI applications to meet specific business needs and challenges.
Building a production-grade user management platform that follows industry best practices including the 12-Factor App methodology, Agile development workflows, and enterprise-level CI/CD pipelines — all while serving as a learning platform for collaborative software engineering.
Architected and deployed a full-stack web application using FastAPI with role-based access control, event-driven user lifecycle management, and comprehensive test coverage. Implemented a complete CI/CD pipeline with GitHub Actions and Docker containerization, publishing production images to DockerHub.
Delivered a fully containerized, production-ready system with automated testing, deployment pipelines, and documentation. The project demonstrates real-world software engineering practices including code review workflows, issue tracking, and contribution guidelines.
AI coding agents lack the ability to autonomously research, store, and retrieve up-to-date information for software projects. Existing tools require manual curation, don't support citation management, and have no structured knowledge persistence layer.
Built a production-grade CLI toolkit following Clean Architecture with strict dependency inversion. The system enables AI agents to query multiple search providers (DuckDuckGo, Serper), generate extractive or AI-powered summaries, manage citations, and maintain a structured research library with full-text search indexing.
Created a fully functional research automation pipeline with commands for querying, refreshing, citing, and onboarding new agents. The architecture supports plugin-style search providers, JSON machine-readable output, and security features including secret redaction in logs — all with zero hardcoded credentials.
Have a project in mind? I'd love to hear about it. Send me a message and I'll get back to you within 24 hours.