class AIEngineer:
def __init__(self):
self.name = "Shyam Baghel"
self.role = "Aspiring AI / LLM Engineer"
self.location = "India 🇮🇳"
self.languages = ["Python", "Bash", "SQL", "Markdown"]
self.current_focus = ["Large Language Models", "Transformer Architectures", "ML Pipelines"]
self.long_term_goal = "AI Research Engineer → Quantum-AI Systems"
self.open_to = ["Open Source", "Research Collaborations", "Internships", "Roles"]
def say_hi(self):
print("Thanks for stopping by! I build intelligent systems and love pushing the boundaries of AI.")
me = AIEngineer()
me.say_hi()- 🧠 Deeply passionate about LLMs, NLP, and AI Research — from theory to deployment
- 🛠️ I write clean, documented Python with a focus on reproducible ML experiments
- 🌱 Currently mastering Transformer architectures, fine-tuning, and RAG pipelines
- 🔭 Long-term: contributing to frontier AI research and quantum-classical hybrid systems
- 🤝 Open to collaborations, open-source contributions, and research mentorship
- ⚡ Fun fact: I believe the best model is the one that generalizes — in ML and in life
My mission is to build AI systems that are not just powerful, but interpretable, ethical, and genuinely useful to humanity.
I'm on a deliberate journey from software engineering fundamentals to the cutting edge of AI research — closing the gap between industry-grade LLM engineering and academic research. I want to contribute to open-source AI tooling, publish research, and eventually work on foundational models or AI safety systems.
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║ MY AI ENGINEERING ROADMAP ║
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║ ║
║ ✅ Python → Core language, OOP, packaging, testing ║
║ ✅ DSA → Algorithms, complexity, problem solving ║
║ 🔄 Machine Learning → Supervised/Unsupervised, scikit-learn, pipelines ║
║ 🔄 NLP → Text processing, embeddings, semantic search ║
║ 🔜 Transformers → Attention, BERT, GPT, fine-tuning, LoRA ║
║ 🔜 LLM Engineering → RAG, agents, prompt engineering, deployment ║
║ 🔜 AI Research → Paper reading, replication, publishing ║
║ 🔮 Quantum AI → Quantum computing + classical AI hybrid systems ║
║ ║
╚══════════════════════════════════════════════════════════════════════════╝
| 📖 Reading | "Attention Is All You Need" + "The Little Book of Deep Learning" + arXiv daily |
| 🏗️ Building | RAG pipeline with LangChain + custom document embeddings using FAISS |
| 🎓 Studying | Fine-tuning LLaMA 3 with LoRA/QLoRA — parameter-efficient training techniques |
| 🤝 Contributing | Open-source ML tooling — documentation, bug fixes, and feature PRs |
| 💡 Exploring | AI Agents, Function Calling APIs, and multi-modal models |
"The best way to learn AI is to build AI."
| Project | Description | Stack | Status |
|---|---|---|---|
| 🐍 python-roadmap | Complete Python learning roadmap — beginner to advanced, with examples and exercises | Python OOP DSA |
🟢 Active |
| 🧠 shyamkr85 | Personal profile repo & experimentation ground for AI/ML concepts | Python Notebooks |
🟢 Active |
| 📊 ml-projects | Collection of ML experiments: supervised learning, pipelines, and model evaluation | scikit-learn Pandas NumPy |
🔜 Planned |
| 🤖 ai-experiments | LLM and NLP experiments — fine-tuning, RAG, and prompt engineering playground | HuggingFace LangChain PyTorch |
🔜 Planned |
| Repository | Type | Description |
|---|---|---|
| 🤗 HuggingFace Transformers | 📝 Docs / 🐛 Bug Fix | Improving documentation and fixing issues |
| 🦜 LangChain | ✨ Feature | Add placeholder for your contribution |
| YOUR_ORG/REPO | 🔧 Feature | Add placeholder for your contribution |
💡 Open to contributing to ML tooling, LLM frameworks, AI safety tools, and research codebases.
| Platform | Profile | Focus |
|---|---|---|
 |
YOUR_USERNAME | DSA — Arrays, Trees, DP |
 |
YOUR_USERNAME | ML Competitions, Notebooks |
 |
YOUR_USERNAME | Python, Problem Solving |
 |
YOUR_USERNAME | Competitive Programming |
- 🥇 [HACKATHON NAME] —
YEAR— [Project Name]: Brief description of what you built - 🥈 [HACKATHON NAME] —
YEAR— [Project Name]: Brief description of what you built - 🎖️ [AI Challenge / Competition] —
YEAR— Participation / Top-N finish
Papers I've read, courses completed, and certifications earned
📄 Papers Read / Being Studied
- Attention Is All You Need — Vaswani et al. (2017)
- BERT: Pre-training of Deep Bidirectional Transformers — Devlin et al. (2018)
- Language Models are Few-Shot Learners (GPT-3) — Brown et al. (2020)
- LLaMA: Open and Efficient Foundation Language Models — Touvron et al. (2023)
- Retrieval-Augmented Generation (RAG) — Lewis et al. (2020)
- LoRA: Low-Rank Adaptation of Large Language Models — Hu et al. (2021)
🎓 Courses & Certifications
- ✅ [COURSE NAME] — Platform (e.g., Coursera / fast.ai / DeepLearning.AI) —
YEAR - ✅ [COURSE NAME] — Platform —
YEAR - 🔄 [COURSE NAME] — Platform —
In Progress - 🔜 CS224N: Natural Language Processing with Deep Learning — Stanford (planned)
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║ ║
║ "The scientists of today think deeply instead of clearly. ║
║ One must be sane to think clearly, but one can think deeply ║
║ and be quite insane." ║
║ ║
║ — Nikola Tesla ║
║ ║
║ Build clearly. Think deeply. Ship boldly. ║
║ ║
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