Work experience
2 roles, most recent first, with the full detail on each.
Built with
- TypeScript
- React
- Node.js
- Express
- PostgreSQL
- pgvector
- Sole engineer taking the platform from a blank repository to production B2B SaaS in under a year, full stack, tracking how brands surface across ChatGPT, Gemini, Claude and Perplexity.
- Rebuilt the scoring pipeline to replace four to five sequential LLM calls with a single structured-output call per result, cutting inference cost around 28% and latency 50–60%.
- Designed the job orchestration behind data collection: 13 job types, exponential backoff, and automatic recovery of stuck runs, so a flaky scrape on one AI engine never takes down a customer's data.
- Built a RAG pipeline on Postgres pgvector with Qwen3 embeddings that grounds content recommendations in the customer's own documents rather than the model's assumptions.
- Published an MCP server exposing 29 tools so AI agents can query a customer's visibility data directly, scoped per customer over OAuth with response payloads trimmed 30%.
- Shipped the multi-tenant layer letting agency partners run the product under their own domain and branding, backed by tiered entitlements gating features, API limits and billing.
Built with
- PL/SQL
- React
- Node.js
- Express
- MongoDB
- Engineered backend procedures in PL/SQL to automate data ingestion and transformation, producing clean structured data for business intelligence and Power BI dashboards.
- Built and maintained full-stack applications on the MERN stack with an eye on scalability and performance.
