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mdadilmuzaffar24/README.md

I am Md Adil Muzaffar, an AI/ML engineer focused on agentic AI and GenAI. I build AI systems end to end: agent design, retrieval, backend APIs, frontend, authentication, deployment and documentation. My flagship project, AZOL AI V1.0, is a multi-agent platform that I designed, built and deployed solo. I also have a published deepfake-detection framework (my M.Tech thesis) and several deployed LLM applications.

"We are moving from training isolated models to deploying autonomous AI systems."

🎓 Education M.Tech in Computer Science (Data Science), Jamia Hamdard University, New Delhi. CGPA 9.0+/10
🚀 Shipped AZOL AI V1.0, a live 8-node LangGraph multi-agent platform
📄 Published Interpretable Deepfake Detection Framework: 98.42% accuracy, 0.9989 ROC-AUC
🔭 Next AZOL AI V2.0: autonomous data intelligence (database connectors, NL-to-SQL, live dashboards)
🎯 Looking for AI Engineer · GenAI / LLM Engineer · AI Agent Engineer · Applied ML Engineer
📍 Based in Patna, India. Open to relocation. Immediate joiner

Featured product: AZOL AI V1.0, live in production

An autonomous multi-agent AI operating system. Give it a goal in plain language. It plans, researches, analyzes, executes and verifies the work, then streams back a result you can inspect.

The API may be asleep when idle, so the first load can take up to a minute while it wakes up. After that it is fast.

Why it is different from a chatbot

A typical AI chatbot AZOL AI
Scope One prompt, one answer One goal, multi-step execution across specialist agents
Trust You fact-check the answer A QA Reviewer node audits claims against sources before delivery
Risk Runs whatever it is asked Human-in-the-loop approval gate (interrupt()) for high-risk steps
Memory Forgets between sessions Persistent project memory (FAISS + PostgreSQL checkpoints)
Isolation Flat chat history Multi-tenant workspaces scoped by user

How a goal flows through the system

flowchart TD
    A([Operator goal]) --> B[Auth and RBAC gate]
    B --> C[Supervisor router<br/>LangGraph]
    C --> D[Planner<br/>task decomposition]
    C --> E[Researcher<br/>live web + FAISS RAG]
    C --> F[Data Analyst<br/>Python sandbox]
    C --> G[Coding Agent<br/>scripts and artifacts]
    D --> H[QA Reviewer<br/>grounding and citation audit]
    E --> H
    F --> H
    G --> H
    H --> I{{Human approval gate}}
    I -->|approved| J[(Persistent memory<br/>FAISS + PostgreSQL)]
    J --> K([Verified, streamed output and artifacts])

    classDef core stroke:#635BFF,stroke-width:2px
    classDef gate stroke:#20D9A0,stroke-width:2px
    class C,H core
    class B,I gate
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What is inside V1.0

Capability Details
🧠 Multi-agent orchestration LangGraph supervisor routes work across Planner, Researcher, Data Analyst, Coding and QA Reviewer nodes
📚 RAG knowledge base 5-stage ingestion for PDF, DOCX, PPTX, CSV and TXT. FAISS with BAAI/bge-small-en-v1.5 embeddings and project-scoped retrieval
⚡ Live workspace React 18 workspace with SSE token streaming, a live React Flow agent graph, and 7 tabs per project (overview, chat, documents, tasks, agents, memory, outputs)
🛡️ Security Google OAuth 2.0, email OTP, HttpOnly JWT cookies, PostgreSQL token-revocation blacklist, rate limiting
📈 Telemetry Execution metrics, token usage and latency views with CSV export
☁️ Deployment Frontend on Vercel, FastAPI on Render, PostgreSQL on Neon. Docker Compose for local infrastructure
📝 Documentation README, 1080p walkthrough video and a 280+ page software design document
🔭 Roadmap: V2.0 and beyond (planned)

V2.0: From AI workforce to autonomous data intelligence

  • Database connectors: read-only access to PostgreSQL, MySQL, MongoDB, Snowflake and CSV/Excel
  • NL-to-SQL and cleaning: schema introspection, query generation, variance analysis and anomaly detection
  • Live BI dashboards: interactive dashboards generated from plain-language questions

Beyond: an autonomous business OS where agents, data and knowledge work together across a team.

Everything in this roadmap is planned. V1.0 is what is live today.


Published research: peer-reviewed, with a live app

Interpretable Deepfake Detection Framework: MTCNN, Xception, Grad-CAM and SHAP. 98.42% accuracy, 0.9989 ROC-AUC, 10,000+ training images, tested on FaceForensics++


More projects, built end to end

AI Financial and Ledger Agent: about 80% less manual invoice entry. LangChain, Llama 3.1 on Groq, Pydantic, Streamlit AI Document Chatbot with RAG: PDF, Word and text. LangChain, FAISS, MiniLM, Streamlit

Autism Spectrum Disorder Screening: 0.96 cross-validated ROC-AUC. XGBoost, scikit-learn, SMOTETomek, SHAP Fake News Detection: 98.4% accuracy with Linear SVM. TF-IDF, scikit-learn


Tech stack: agents, ML, backend, frontend, cloud

Tech stack: LangGraph, LangChain, RAG, MCP, Groq, Ollama, FAISS, Python, PyTorch, TensorFlow, Keras, scikit-learn, XGBoost, OpenCV, FastAPI, PostgreSQL, Redis, Docker, React, Vite, Tailwind, Vercel, Render, Neon, Azure AI, Git


Experience, 2023 to present

Founder & Independent AI Systems Engineer, AZOL AI · 2026 to present, self-directed
Designed, built, deployed and documented a production multi-agent platform end to end: LangGraph orchestration, RAG, security, a React workspace, cloud deployment and a 280+ page design document.

Artificial Intelligence Intern, Edunet Foundation (Microsoft, AICTE) · Apr 2025 to May 2025
Built the XGBoost autism-screening model (0.96 CV ROC-AUC) and deployed an image classifier on Azure Custom Vision.

Data Science Intern, Internshala Trainings (Remote) · Jun 2023 to Oct 2023
Built a weather-forecast model in Python, analysed IPL ball-by-ball data with SQL for an auction-strategy simulation, and built a Tableau sales dashboard.


Education and credentials: M.Tech and 10+ certifications

M.Tech, Computer Science Engineering (Data Science), Jamia Hamdard University, New Delhi · 2024 to 2026 · CGPA 9.0+/10
B.Tech, Electrical & Electronics Engineering, MACET, Patna · 2018 to 2022 · CGPA 7.6/10

🎓 Certifications (10+)
Certification Issuer Date
Agentic AI with LangChain and LangGraph IBM Sep 2026
Introduction to Model Context Protocol · MCP: Advanced Topics Anthropic Aug 2026
MLOps and Data Pipeline Orchestration for AI Systems LinkedIn Learning Jul 2026
Deep Learning Specialization DeepLearning.AI (Andrew Ng) Apr 2026
Machine Learning Specialization Stanford University Aug 2025
IBM Data Science Professional Certificate IBM / Coursera Jun 2025
AI/ML for Crop Acreage Mapping ISRO IIRS Apr 2025
Generative AI for Data Science Microsoft Mar 2025
Python for Data Analysis and Visualization Coursera Mar to Apr 2025
Microsoft Azure AI Essentials Professional Certificate LinkedIn Learning

Community: AI Updates on LinkedIn and Instagram

I run the AI Updates series on LinkedIn and Instagram, explaining agentic AI and frontier-model research with verified data points to an audience of 1,500+ followers.


Let's build something

I am looking for AI Engineer, GenAI Engineer and AI Agent Engineer roles, and I can join immediately.

LinkedIn Portfolio Email


I don't just train models. I engineer and ship AI systems to production. Md Adil Muzaffar, AZOL AI.

If AZOL AI or any of these projects is useful to you, a ⭐ on the repo is appreciated.

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  1. AZOL-AI AZOL-AI Public

    Autonomous Multi-Agent AI Operating System — Orchestrating specialized LangGraph agents, 5-stage FAISS vector RAG, live web intelligence, and verified execution via FastAPI, PostgreSQL & React.

    JavaScript 1