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AI/ML Engineer and Computer Science undergraduate specializing in Generative AI, Natural Language Processing (NLP), Large Language Models (LLMs), Computer Vision, Deep Learning, and autonomous intelligent systems. I focus on building real-world AI products, including RAG pipelines, transformer-based architectures, and agentic workflows that integrate reasoning, memory, and tool usage. My work revolves around:
I actively work with PyTorch, Hugging Face, FastAPI, LangChain, and vector databases, turning research into deployable systems. |
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🎓 AI/ML Engineer & CS student at University of Mianwali (CGPA: 3.58/4.0) 🤖 Focused on Generative AI, NLP, LLMs, CV, and Deep Learning ⚙️ Built RAG pipelines, transformers, and AI agents 🚀 Experienced with PyTorch, Hugging Face, FastAPI, LangChain 🔬 Interested in AI research and production ML systems 💡 Passionate about intelligent automation and real-world AI solutions |
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FULL NAME: Malik Muhammad Mudassir Iqbal
ROLE: AI Research Engineer / ML Systems Developer
LOCATION: Pakistan 🇵🇰
MISSION:
Build intelligent autonomous systems that learn, reason, and act
FOCUS AREAS:
- Large Language Models (LLMs)
- Agentic AI Systems
- Generative AI
- NLP + RAG Pipelines
- Computer Vision
- AI Product Engineering| 🔥 FIELD | 🧠 SKILLS |
|---|---|
| 🧾 NLP & LLMs | Transformers, Prompt Engineering, RAG |
| 🤖 Agentic AI | LangChain, SmolAgents, Tool Calling |
| 🧠 LLM APIs | OpenAI API, Claude API, Groq API |
| 📚 AI Frameworks | HuggingFace, LangChain, Transformers |
| 👁️ Computer Vision | CNNs, Image Classification |
| 🚀 GenAI | Text Generation, AI Agents |
| ⚙️ Deployment | Flask, Streamlit, Docker |
✔ LLM Chatbots → Prompt Engineering + OpenAI + LangChain
✔ AI Agents → SmolAgents + Tool Calling + Workflow Design
✔ RAG Systems → HuggingFace + Vector Databases + Retrieval Pipelines
✔ NLP Apps → Transformers + Tokenization + Fine-tuning
✔ AI APIs Integration → Claude + OpenAI + Groq orchestration
✔ CV Projects → CNN models + Image preprocessing
🧠 Transformer Architectures & LLM Optimization
📚 Retrieval-Augmented Generation (RAG Systems)
🤖 Autonomous Agent Systems (Agentic AI)
👁️ Computer Vision & Deep Learning
🚀 Generative AI Applications
⚙️ Production AI Engineering
▶ Building multi-agent AI systems
▶ Working on LLM orchestration pipelines
▶ Developing RAG-based knowledge systems
▶ Exploring AI tool-use & function calling
▶ Deploying production-ready AI apps
✔ Phase 1: Python + Math + DSA
✔ Phase 2: Machine Learning + Data Science
➤ Phase 3: Deep Learning + NLP + CV
➤ Phase 4: LLMs + GenAI + RAG Systems
➤ Phase 5: Agentic AI + Production AI Systems
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🔥 “I don’t just train models — I engineer autonomous intelligence systems that evolve.”