Jatin Saini

A collaborative task management platform using Next.js, React, TypeScript, and Firebase with real-time updates and secure authentication.

• Implemented task assignment, workspace collaboration, and integrated a Node.js + Express backend with Firestore for scalable and efficient data handling.

A real-time speech translation application that converts Hindi audio to English text using advanced AI models.

• Built a React + TypeScript speech translation app using MediaRecorder to capture audio and Groq's Whisper API to translate Hindi speech to English text.
• Implemented async API handling with FormData uploads, error handling for API failures and microphone access, and state management for recording, loading, and translation states.

A browser extension that generates concise summaries of webpage content using the Google Gemini API.

• Built a browser extension using JavaScript that extracts webpage content and sends it to the Google Gemini API to generate concise summaries.
• Injected custom scripts into active tabs, handled DOM parsing, and implemented API integration for real-time, AI-powered summarization.

Engineered a meta-agent with the Google Agent Development Kit (ADK) that can dynamically generate executable Python scripts for specialized AI agents based on natural language descriptions.

• Demonstrated proficiency in agentic workflows by building a single agent that can perform a complex, multi-step task and also serve as a general conversational assistant.
• Showcased an understanding of multi-agent system principles and conditional logic, allowing the agent to dynamically switch between acting as a builder or a chatbot based on user intent.

A Retrieval-Augmented Generation (RAG) chatbot designed to deliver context-aware responses over private datasets.

• Configured and deployed a local Llama 3.1:8B model using Ollama for low-latency inference without relying on external APIs.
• Configured Flowise nodes for embeddings, vector search, and context-aware Llama responses.
• Explored modern LLM concepts including embeddings, prompt engineering, and the overall architecture of RAG pipelines.

A production-ready AI-powered REST API that analyzes resumes against job descriptions and returns structured insights.

• Built a scalable Node.js + TypeScript REST API using clean architecture, decoupling business logic from LLM providers via interfaces and factory patterns.
• Implemented secure API key authentication, per-key rate limiting, usage tracking, and expiry to simulate real-world SaaS API behavior.
• Integrated Groq LLM for resume analysis and deployed the service on Render with environment-based configuration.