I'm an AI-focused Full-Stack and Backend Developer building practical GenAI products using Python, FastAPI, React, TypeScript, RAG, LLM tools, and AI agents.

I enjoy building systems where LLMs can retrieve knowledge, use tools, interact with databases, automate workflows, and power real-world applications.

Currently, I am focused on Agentic AI, RAG systems, LLM tool/function calling, full-stack AI products, and backend engineering.

• AI agents and tool-calling systems
• RAG applications with source-grounded responses
• Full-stack AI products with React, TypeScript, and FastAPI
• Backend APIs for LLM and data-driven applications
• Natural-language interfaces for databases and dashboards
• Applied machine learning and automation workflows

Languages: Python, JavaScript, TypeScript, SQL

AI / GenAI: LangChain, LangGraph, LlamaIndex, RAG, AI Agents, Tool Calling, LLM Applications

Backend: FastAPI, REST APIs, PostgreSQL, Supabase, SQLAlchemy, API Integration

Frontend: React, TypeScript, TailwindCSS, Recharts

Tools: Git, GitHub, Docker, ChromaDB, Pytest, Ruff, VS Code

A full-stack AI-powered business intelligence platform where users can connect databases, ask questions in natural language, generate SQL queries, and visualize results through dashboards.

Highlights:

• Natural language to SQL query generation
• AI-agent-based query reasoning workflow
• Database connection and dashboard management
• Interactive frontend visualizations

Tech: React, TypeScript, FastAPI, LangGraph, Supabase, Recharts, Groq/OpenAI-compatible LLMs

A structure-aware document retrieval system designed to generate accurate, source-grounded answers from complex PDFs while preserving their natural hierarchy and content structure.

Highlights:

• Parses complex PDF documents using Docling
• Preserves headings, paragraphs, tables, equations, and section hierarchy
• Uses hierarchical parent-child chunking instead of basic fixed-size splitting
• Retrieves relevant document sections with traceable source context
• Provides a FastAPI backend for document ingestion, retrieval, and question answering
• Designed with persistent storage, testing, and production-oriented architecture in mind

Tech: Python, FastAPI, Docling, SQLAlchemy, PostgreSQL, Vector Databases, RAG, LLMs, Docker

An AI-powered e-commerce operations platform designed to automate store workflows, assist operational teams, and coordinate AI agents across orders, inventory, customer support, and business analytics.

Highlights:

• Automates repetitive e-commerce and store-management workflows
• Uses AI agents to reason about orders, inventory, support requests, and operational data
• Supports human approval for sensitive or high-impact agent actions
• Provides backend services for workflow execution, integrations, and task processing
• Designed around multi-tenant access, role-based permissions, and auditability
• Uses background workers for long-running and asynchronous operations

Tech: Python, FastAPI, LangGraph, PostgreSQL, SQLAlchemy, Redis, Celery, React/Next.js, TypeScript, Docker

• Building production-ready GenAI and full-stack AI applications
• Improving RAG pipelines and AI agent workflows
• Creating reusable LLM tools and function-calling systems
• Strengthening backend engineering with FastAPI, PostgreSQL, Docker, and testing
• Preparing for AI Engineer, GenAI Developer, Python Backend, and Full-Stack Developer roles

• GitHub: github.com/danishali778
• LinkedIn: linkedin.com/in/danish-ali-dev
• Email: danish.ali.73400@gmail.com