Kabir Jaipal
Professional Summary
Full Stack AI Engineer with 2+ years of experience building web applications, backend APIs, and multi-agent workflows. Proficient in MERN Stack (MongoDB, Express.js, React, Node.js), Next.js, TypeScript, Python (FastAPI), PostgreSQL (pgvector), and Redis (BullMQ), with a foundational understanding of data structures, algorithms, and system design. Experienced in integrating LLM APIs (OpenAI, Claude, Gemini, Groq) with RAG, Tool Calling, and structured outputs, using modern agentic tools like Cursor and Claude Code for rapid planning and development.
Technical Skills
Work Experience
Developed full-stack web applications, AI-driven presentation workflows, and internal task management systems.
- Built internal task management systems and reporting dashboards using Node.js/Express and PostgreSQL, enabling task assignment workflows and optimizing SQL queries to cut load times by 45%.
- Engineered an AI-powered presentation builder with Tiptap editor to generate slide decks from prompts, building a Puppeteer export pipeline to export PDF presentations in under 15 seconds.
- Integrated LLM APIs to extract unstructured data from complex PDFs into structured JSON schemas with 98% accuracy, and built web scrapers for SEO content generation using Python.
- Developed a Request for Proposal (RFP) platform with real-time chat (WebSockets), AI vendor scoring, and automated email alerts, reducing proposal turnaround time by 40%.
Projects
AI-driven learning platform that generates customized course syllabi and interactive lessons.
- Built an automated course generation feature using Next.js App Router and OpenAI API with token usage logging, achieving sub-3-second lesson generation times.
- Defined database schemas in PostgreSQL using Prisma ORM and integrated NextAuth.js authentication and Stripe Webhooks for user subscriptions.
- Containerized the application and database with Docker Compose to simplify local development and automated database schema migrations.
Full-stack AI application for document search (RAG) and background task processing.
- Developed a document search application using Next.js, Express, and PostgreSQL (pgvector) with LangChain for vector similarity retrieval.
- Implemented asynchronous document processing using BullMQ and Redis queues to handle text chunking in the background, keeping API endpoints responsive under 200ms.
- Configured multi-container development environments using Docker Compose, integrating Better Auth for user authentication and AWS S3 for document storage.
API health monitoring tool with background checks, real-time alerts, and automated failure summaries.
- Built background API monitoring tasks using FastAPI BackgroundTasks and Redis, implementing result caching to reduce database read queries by 85%.
- Implemented an incident diagnostic feature using the Groq API (JSON mode) to summarize error logs, broadcasting live status updates via Redis Pub/Sub WebSockets.
- Configured Docker Compose setup with Nginx reverse proxy to route HTTP and WebSocket traffic across services.