I'm an engineer working the seam between engineering and the customer - building the security data pipelines and detection systems behind enterprise deployments, then running the POCs and simulation environments that prove they work.
I'm a cybersecurity engineer based in Los Angeles, building data infrastructure for security products. At Gurucul, I design and run the production pipelines that move customer logs into a SIEM, own the simulation environments behind enterprise demos, and work directly with customers to run POCs and workshops.
What makes that work is the engineering underneath it - I build the production data pipelines and detection use cases myself, so when a prospect asks a hard technical question in the room, I can answer it, or go change the platform to prove it.
Before Gurucul, I completed my Master's in Computer Science at USC, where I built ML pipelines for user behavior analytics. I'm now looking at Forward Deployed Engineer, Solutions Engineer, and Software Engineer roles where that same mix - hands-on build plus customer-facing delivery - is the job.
A Docker Compose Kafka stack with a Python consumer that normalizes timestamps, extracts device, IP, and locale fields, and republishes cleaned login events to a second topic, with error handling, logging, and graceful shutdown.
Survey paper covering SQL injection, insider threats, data leakage, and ransomware. Analyzed real-world breaches - Equifax, Capital One, Colonial Pipeline - to evaluate encryption, RBAC, and DLP mitigations.
A custom query language with multiple natural-language verbs per SQL keyword (pick, fetch, grab, retrieve) and a parser that translates each query to SQL, served by a Node.js and Express REST API with a React and TypeScript frontend.
Android safety application built for real-time community patrol coordination, with real-time data integration and user-facing safety features.
Multi-platform app - Android plus two web clients - connecting users to local businesses through the Yelp API, on a shared Node.js/Express backend.
A Kafka consumer that filters and reshapes streaming login events on the fly - normalizing timestamps, flagging noteworthy app-version patterns, and republishing clean records downstream with production-grade error handling and logging.
The open-source pipeline behind my USC anomaly-detection work: generates activity data, trains an Isolation Forest to flag outliers, scores normal-vs-anomalous with logistic regression, and surfaces results in a Dash dashboard.
Open to Software Engineer, Forward Deployed Engineer, and Solutions Engineer roles - California, Washington, New York, New Jersey, Florida, Texas and Remote.