I’m a Staff-level Fullstack Engineer with 8+ years of experience building scalable systems across AI, data, and product infrastructure - primarily in early-stage and fast-paced environments.
Took ownership of an ambiguous product vision to automate SEO with generative AI, independently architecting and delivering a full-stack, production-grade platform from conception to launch.
Built an LLM-powered agentic system using LangChain, LangGraph, OpenAI APIs, NestJS, and Postgres, supporting autonomous workflows for page scraping, content diffing, keyword generation, markdown creation, and page content optimization.
Developed tooling that enabled product managers and subject matter experts to co-own prompt engineering and debugging workflows, bridging the gap between technical and domain expertise.
Started as the sole engineer on the project, and built and mentored a team of 5 that evolved into the company’s dedicated Client Marketing Automation group.
Shipped the core system that now powers SEO recommendations and AI-generated content across 30,000+ client websites, transforming a manual service into an intelligent, scalable product.
2023 — 2024
Brought on in a full-time, time-bound capacity to fill a critical engineering gap during a transitional hiring phase.
Played a key role as the second engineer, shaping the architecture, roadmap, and engineering culture of Coderbyte’s developer upskilling and technical interview platform.
Led development of core product features including the coding challenge engine, test case validation framework, and real-time code execution infrastructure across multiple languages.
Collaborated closely with product and marketing to translate user feedback into new capabilities - helping expand Coderbyte from a question bank into a full-stack developer training platform.
2019 — 2024
Created and scaled Coderbyte’s YouTube channel, growing it to 28,000+ subscribers through high-quality videos teaching software engineering, interview prep, and CS fundamentals.
Acted as a community facilitator and technical mentor, regularly engaging with users, reviewing submissions, and guiding engineers through common patterns and anti-patterns in real-time feedback sessions.
New York, United States
Architected and led development of a high-throughput ETL data pipeline that normalized heterogeneous international credit bureau data into a unified schema, enabling accurate, real-time financial insights.
Spearheaded the global credit integration strategy, launching connectivity with Experian (USA), TransUnion (South Africa), Círculo de Crédito (Mexico), and Credit Bureau Singapore, extending the platform reach across multiple geographies.
Built the Credit Map Studio, a self-serve developer platform that empowered engineers across teams to build, validate, and deploy credit data transformations with automated testing and schema evolution tracking.
Partnered with Data Science to co-develop Nova’s flagship Global Credit Score product - an ML-informed creditworthiness model used by Fortune 500 banks to underwrite loans for immigrants with no domestic history.
Designed and implemented microservice-based systems using AWS Lambda, API Gateway, and DynamoDB, adhering to a fully serverless architecture to support high-volume, low-latency cybersecurity workflows.
Launched and productionized ML-driven threat detection algorithms, integrating model inference into real-time Lambda pipelines scanning customer endpoints in real time for anomalous behavior.
Developed internal tooling to support model rollout, confidence threshold tuning, and post-deployment alert review, bridging ML outputs with incident response workflows.
Migrated a legacy monolithic frontend to a modern React + GraphQL stack hosted on AWS Amplify, reducing annual platform maintenance costs by ~$100K.
Spearheaded development of a customer-facing portal displaying scan results, threat signatures, and remediation recommendations, increasing customer engagement and transparency.
Education
2012 — 2016
Yeshiva University
Biology
2012 — 2016
Grace Hopper Academy