I build streaming pipelines
Software engineer and CS M.S. student at CCNY with an Information Security background. I design backend, data and AI systems, and ship them deployed, tested and measured.
I'm a software engineer and Computer Science M.S. student at The City College of New York, where I also teach undergraduates as an Adjunct Lecturer. My background is in Information Security, so I build systems that are secure by design: least privilege, validated inputs, and guardrails that don't depend on anything behaving nicely.
I like problems that span the stack: a Kafka stream that has to keep up, an AI agent that must not be talked into things, a data pipeline whose numbers have to add up. Every project here is open source, has a live demo, and has CI that reproduces its published numbers, because I'd rather show a result than claim one.
Teaching 120+ students has made me good at the part engineers often skip: explaining clearly why something works.
From information security in Vellore to teaching and building in New York.
Each one is open source, deployed, and checked by CI against its published results. The badges are live from GitHub.
A Kafka pipeline that scores card transactions for spending bursts, unusual amounts and impossible travel, running on Docker Compose and Kubernetes with Prometheus and Grafana monitoring.
Ask a question by typing or voice and get the exact sentence it came from, with its source. Hybrid BM25 and embedding search with a learned re-ranker over 19K Wikipedia passages.
A LangGraph agent that answers payment-risk questions with read-only SQL, policy-search and calculator tools, shows every step it takes, and has guardrails that stop it being talked into actions.
Turns real scanned receipts into structured records with multi-pass OCR voting and learned field scorers, and routes anything uncertain to human review. Deployed as a serverless AWS SAM stack.
A medallion Delta Lake over 541K real retail transactions with incremental MERGE loads and data-quality gates, feeding a Spark ML churn model tracked in MLflow.
Every skill here is backed by a project, a job or a certification. Matching projects light up.
This assistant answers questions about my experience, projects and skills. Like my RAG project, it only answers from what's actually on this site, and it links to the source. If it doesn't know, it says so.
It runs entirely in your browser: no AI service, nothing you type is sent anywhere.
Open to software engineering internships. Your message lands straight in my inbox.
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