Applied AI Engineer — I build LLM systems, ship them to production, and make sure they don't hallucinate.
I'm an Applied AI Engineer focused on shipping LLM-powered systems — RAG pipelines, multi-agent workflows, and document intelligence. My work lives at the intersection of "does it actually retrieve the right thing" and "can we prove it didn't hallucinate."
- 🏆 IBM Granite Hackathon — Winner (Team PolyEns, agentic lead-generation)
- 🚀 11 AI systems shipped across 15 international hackathons in 2024–2026
- 🎓 Stanford Code in Place — Section Leader (selected from 900+ global applicants)
- 🔬 AlgoVerse Research Program (EOE.ai × Omdena) — 45% merit scholarship, RAG/NLP research
- 📍 Pakistan · Open to remote AI Engineer / GenAI / LLM Engineer roles
I don't list technologies I haven't shipped with. Every stack on this page appears in a repo you can open.
| Domain | What I've Shipped |
|---|---|
| RAG Systems | Multi-agent RAG with groundedness critic · Enterprise doc Q&A · Compliance retrieval |
| AI Agents | Multi-agent lead qualification (debate pattern) · Job-application agent · Requirement-classification agent |
| Document Intelligence | PDF → structured insight extraction · Resume ATS scoring · Contract fit-gap analysis |
| LLM Applications | 8+ live Hugging Face Spaces · Groq / LLaMA 3 / IBM Granite / HF Inference API |
🧪 ComplianceRAG — RAG with a Groundedness Critic
Most RAG demos hide when retrieval fails. This one measures it.
The problem: RAG systems produce fluent answers that quietly drift from source material. In compliance, that's a lawsuit.
The system: Three-agent pipeline — Retrieval Agent (FAISS + all-MiniLM-L6-v2) → Generation Agent (HF Inference API, cited answers [1][2]) → Critic Agent (independent sentence-level groundedness scoring + citation validation). Low-confidence answers are auto-flagged for human review.
Stack: Gradio · sentence-transformers · FAISS · Hugging Face Inference API · PyPDF
Why it matters: Shows the orchestrator / specialist / critic pattern used in production multi-agent systems — not just retrieve-and-generate.
🏆 AdvancedLeadsGeneration-AI — IBM Granite Hackathon Winner
Two AI agents argue about your leads. The one that wins is the one grounded in your historical deals.
The problem: CRM lead-scoring uses static rules and ignores negotiation nuance.
The system: Multi-agent debate architecture — one agent advocates for the lead, another surfaces risks. A reconciliation layer scores against historical deal data. Integrated with IBM Watson AI on IBM Granite models.
Stack: Next.js · FastAPI · IBM Watson AI · IBM Granite
Why it matters: Ships against a judged bar under time pressure. Team PolyEns won the Generative AI Hackathon with IBM Granite.
🎯 HireMind-AI — LLM-Powered Hiring Copilot
Multi-feature product, not a single-endpoint demo.
The problem: Candidates optimize resumes blind — no visibility into ATS logic or multi-role comparison.
The system: 8 integrated surfaces — multi-JD analysis, ATS scoring engine (skills % + keyword % + experience alignment), skill-gap detection, resume optimizer, cover-letter generator, interview prep, application tracker, career chat.
Stack: Streamlit · Groq API (LLaMA 3.1) · JSON persistence · Hugging Face Spaces
Why it matters: Demonstrates end-to-end product scoping — not "here's a chatbot," but "here's a career workflow."
- Ship fast, evaluate honestly. Every RAG I build gets a groundedness check. Every agent gets a critic.
- Retrieval quality > prompt cleverness. Most "prompt engineering" problems are actually retrieval problems.
- Live demo or it didn't happen. 8 of my projects have public Hugging Face Spaces you can break right now.
- I only list what I've built. If a stack is on this page, there's a repo behind it.
| Event | Project | Result |
|---|---|---|
| Generative AI Hackathon with IBM Granite | AdvancedLeadsGeneration-AI | 🏆 Winner |
| Fall in Love with DeepSeek | DevAI | 🥉 Finalist |
| Replit & Cursor Hackathon | Byte Busters | 🥉 Finalist |
| Agentic AI with IBM watsonx Orchestrate | AI SOC Security Analyst | Participant |
| Co-Creating with GPT-5 | Agentica (multi-agent orchestration) | Participant |
| RAISE YOUR HACK | HealthBridge | Participant |
| AI for Connectivity Hackathon II | KONEKTA | Participant |
| AIstronauts: Space Agents | ARCANA Space Agent | Participant |
| Qubic Hack the Future | AdmitWise | Participant |
Full profile: lablab.ai/u/@Faraz_Mubeen — 15 events, 11 submissions.
- Stanford University — Code in Place · Section Leader (2025). Selected as 1 of 100 from 900+ global applicants. Led weekly live Python sessions for a global beginner cohort.
- AlgoVerse Research Program (EOE.ai × Omdena) · Accepted with 45% merit scholarship. Contributed to RAG / LangChain / Hugging Face research prototypes.
- Founder Institute Pakistan · Cohort 2025 (health-informatics track, KnullAI).
Languages · Python · SQL · TypeScript
LLMs & APIs · Groq (LLaMA 3, 3.1, 3.3 70B) · IBM Granite · IBM Watson · Hugging Face Inference API · OpenAI API
RAG / Vector · FAISS · sentence-transformers (all-MiniLM-L6-v2) · LangChain · PyPDF
Agents · Multi-agent debate patterns · Critic/verifier agents · CrewAI (Agentica)
Backend · FastAPI · REST APIs
Frontend / Demo · Streamlit · Gradio · Next.js
Deployment · Hugging Face Spaces · Streamlit Cloud
- 📧 Email: [email protected]
- 💼 LinkedIn: linkedin.com/in/farazmubeenhaider
- 🤗 Hugging Face: huggingface.co/Faraz618
- 🧪 Hackathons: lablab.ai/u/@Faraz_Mubeen
- ✍️ Writing: medium.com/@farazmubeenhaider902