Retrieval-Augmented Generation
Projects show document ingestion, vector search, grounded answers, and source-aware AI workflows.
AI Engineer | RAG Systems | LLM Apps
Nadeem
I build AI systems that retrieve, reason, extract, and ship.
I specialize in production-style RAG pipelines, document intelligence, vision AI, conversational analytics, and full-stack interfaces that make model output usable.
Open to AI Engineer roles
RAG systems
LLM apps
Document AI
AI product backend
Hiring Signal
AI engineering proof, not buzzwords.
Hiring teams need to see whether an AI engineer can define the system boundary, connect models to real data, handle failures, and ship a usable product. This portfolio is organized around those signals.
Target RoleAI Engineer / GenAI Engineer
Core ProofRAG, document extraction, LLM interfaces
DeliveryPython, Flask, Streamlit, APIs, web UI
EdgeFull-stack + embedded systems breadth
LLM Document Intelligence
Vision and extraction pipelines turn messy financial or legal documents into structured outputs.
Productized AI Interfaces
Flask, Streamlit, Chart.js, and responsive UI work make AI tools usable outside notebooks.
What a reviewer should see in 60 seconds
Not another chatbot wrapper.
- AI projects solve document, retrieval, and analysis problems.
- Repositories are linked directly for code inspection.
- Each featured project states the system boundary and hiring signal.
- Full-stack delivery proves the model work can become usable software.
AI Proof Of Work
Selected AI projects.
Curated for recruiter and engineering review: real problems, inspectable repositories, clear system boundaries, and production-style decisions.
Signature AI ProjectPython | Flask | OpenAI Vision | LangChain
Credit Card AI Analyzer
Document intelligence app that uses vision AI to parse credit card statements, extract billing metadata, and turn raw statement data into readable financial summaries.
- Problem
- Financial documents are messy, visual, and hard to convert into structured user-facing data.
- AI Signal
- Shows multimodal extraction, LLM orchestration, prompt boundaries, and app delivery.
RAG ArchitecturePython | Streamlit | Pinecone | LangChain
Legal AI RAG Assistant
Document-grounded assistant that retrieves legal context from vector storage and answers through a retrieval-first architecture built for traceable outputs.
- Problem
- Legal Q&A needs grounded context instead of generic model responses.
- AI Signal
- Shows vector search, retrieval design, answer grounding, and source-oriented UX.
AI Data AnalystPython | Flask | Pandas | Chart.js
InsightFlow CSV Analyzer
Conversational analytics tool for uploaded CSV files, translating plain-English questions into statistics, summaries, and dynamic charts.
Open RepositoryAI-Adjacent SystemsC++ | ESP32 | FreeRTOS
ESP32 4-DOF Robot Arm
Robotics control software with dual-core task design, servo control, inverse kinematics, telemetry, and a browser-based control interface.
Open RepositoryProduct EngineeringStatic Site | Firmware Flow
Udaan Aviation
Drone engineering product site with firmware download flow, component presentation, product actions, and responsive UI delivery.
Open RepositoryMore builds
InsightFlow CSV AnalyzerConversational analytics | Python, Pandas, Chart.js
RoboEyes ESP32 DashboardEmbedded web control | ESP32, OLED, WebSockets
ESP DroneFlight firmware exploration | ESP-IDF, ESP32
Rescue Call Emergency SystemHealthTech emergency workflows | React Native, Firebase
Subscription DashboardOperational dashboard UI | HTML, CSS, JavaScript
Core Stack
01
LLM & RAG Engineering
LangChain, Pinecone, OpenAI vision workflows, chunking, retrieval design, prompt control, document parsing, and grounded answers.
02
AI Product Backend
Python, Flask, Streamlit, REST APIs, Pandas, JSON extraction, data validation, file upload flows, and deployable app structure.
03
Full-Stack Delivery
React, Node.js, Express, MongoDB, Docker, responsive UX, dashboards, and interfaces that make AI outputs useful to humans.
Contact Channel
Hiring for AI engineering?
I am open to AI Engineer, GenAI Engineer, RAG Engineer, and full-stack AI product roles.