• 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.

• 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.

• 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.

• 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.