Building production-grade agentic AI systems with CrewAI, LangGraph, and MCP Servers — shipping LLM-powered automation for legal, finance, sales, and HR at scale.
I'm an AI Engineer with 3+ years of production experience and an applied AI Researcher focused on the rapidly evolving frontier of agentic systems. I build the layer between raw foundation models and real business outcomes — RAG pipelines, multi-agent orchestration, and the Python back-end plumbing that makes LLMs reliable at scale.
- 🤖 Engineering focus: Agentic AI (CrewAI, LangGraph, MCP), Production RAG, LLM Automation
- 🔬 Research focus: Agent reliability, RAG evaluation, cost-aware LLM routing, MCP-based tool use
- 🏗️ Currently building: Multi-agent sales intelligence, custom MCP servers, fine-tuned vision pipelines
- 📝 Writing: LLM architectures, Seq2Seq models, NLP preprocessing, PaLM — read on Medium →
- 📫 Reach me: huzaifatahir7524@gmail.com
"AI isn't the future — it's the now. Let's build it."
| Project | Result |
|---|---|
| Deep Research Sales Intelligence Agent (CrewAI + FastAPI) | Top-ranked among client deliverables; autonomous B2B lead qualification |
| Payroll Automation Pipeline (Python + LLM) | 90% reduction in manual processing time; zero calculation errors |
| B-Master Multi-Agent Platform (CrewAI + LangChain + LangGraph) | ChatGPT-style analytics across multiple agent stores |
| GPT-4 Vision Fine-tuning (vehicle detection) | 74% classification accuracy → automated price estimation |
| Cowinai Interview Copilot (Deepgram + Groq) | Sub-second real-time conversational coaching |
| Custom MCP Servers (OpenAI-integrated) | Modular agent workflow orchestration, lower integration overhead |
I publish applied research and technical deep-dives on Medium — focused on LLM internals, retrieval systems, and the pragmatic engineering behind production AI.
| # | Article | Topic |
|---|---|---|
| 1 | LLM: Large Language Models — A Comprehensive Guide | LLM architecture & applications |
| 2 | Understanding Seq2Seq Models: Revolutionizing Language Processing | Encoder-decoder models for NLP |
| 3 | Unleashing the Potential of Language: Introducing PaLM | Google's Pathways Language Model |
| 4 | Demystifying Principal Component Analysis (PCA) | Dimensionality reduction |
| 5 | Decoding the Magic: NLP Tokenization and Text Normalization | NLP preprocessing fundamentals |
Current research threads:
- Agent reliability and failure-mode taxonomy in multi-agent CrewAI/LangGraph systems
- RAG evaluation beyond surface retrieval metrics
- Cost-aware LLM routing for mixed open-source + proprietary stacks
- MCP-based tool use patterns in production agents
📬 Open to research collaborations and guest-author opportunities.
Source Code · Python · LangChain · OpenAI · Streamlit · SQL
Natural-language interface for Exploratory Data Analysis — users query datasets in plain English and receive statistical summaries, correlation heatmaps, and ML-ready insights via the LangChain SQL Agent.
Source Code · Python · LangChain · ChromaDB · GPT-4 · Groq
Generates personalized study plans and condenses full-length books into structured summaries using chunked ChromaDB retrieval — designed to overcome GPT-4's context-window constraints.
Source Code · Hugging Face · OpenAI Whisper · PyTorch
Fine-tuned OpenAI Whisper on a curated Arabic speech dataset using Hugging Face Transformers for accurate Arabic ASR transcription, benchmarked on held-out test audio.
Live Site · OpenAI · LangChain · Django · RAG · AWS EC2
Production RAG chatbot for an American high-school academy — supports PDF uploads, automated question generation, and staff–student communication. Hosted on AWS EC2 for production availability.
- Machine Learning Specialization — DeepLearning.AI / Coursera (2023)
- Natural Language Processing Specialization — DeepLearning.AI / Coursera (2023)
I'm always open to conversations about agentic AI, production RAG, LLM fine-tuning, or research collaborations. If you're building something interesting, reach out.
⭐ If you find my work useful, consider starring a repo — it helps others discover it too.