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 |
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.
| 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 |
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
| 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.
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.
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 |
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.

