class MLEngineer:
def __init__(self):
self.name = "Abdul Nazeer M"
self.role = "Machine Learning Engineer"
self.location = "India 🇮🇳"
self.languages = ["Python", "SQL", "JavaScript"]
self.specializations = [
"Deep Learning", "NLP", "Computer Vision",
"Generative AI", "LLM Fine-tuning", "MLOps",
"LLM-powered Applications", "Data Engineering"
]
self.current_focus = "Fine-tuning LLMs & building intelligent applications"
def get_daily_routine(self):
return {
"morning": "☕ Coffee + Research papers",
"afternoon": "💻 Model training & optimization",
"evening": "📊 Data analysis & visualization",
"night": "🧠 Learning new AI frameworks"
}- 🎯 LLM Fine-tuning - Custom domain-specific language models using LoRA, QLoRA & PEFT
- 🤖 LLM-powered Applications - Building intelligent chatbots, code assistants & RAG systems
- � Advancied NLP Models - Context-aware conversational AI with memory & reasoning
- ⚡ MLOps Pipeline - Automated model deployment, A/B testing & monitoring
- 📊 Real-time Analytics - Streaming data processing with ML insights
- 🧠 Research Projects - Exploring efficient fine-tuning techniques & model optimization
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graph LR
A[LLM Fine-tuning] --> B[LoRA/QLoRA]
B --> C[RLHF]
C --> D[Custom Models]
E[LLM Applications] --> F[RAG Systems]
F --> G[AI Agents]
G --> H[Tool Integration]
I[Advanced MLOps] --> J[Model Monitoring]
J --> K[A/B Testing]
K --> L[Production LLMs]
M[Multimodal AI] --> N[Vision-Language]
N --> O[Code Generation]
O --> P[Reasoning Systems]
| Fine-tuning Techniques | LLM Applications | Optimization Methods |
|---|---|---|
| 🎯 LoRA & QLoRA | 🤖 Chatbots & Assistants | ⚡ Quantization |
| 🔧 PEFT Methods | 📚 RAG Systems | 🚀 Model Compression |
| 🎓 RLHF Training | 🛠️ Tool-using Agents | 💾 Memory Optimization |
| 📊 Custom Datasets | 💬 Conversational AI | 🔄 Efficient Inference |
"Building the future, one algorithm at a time" 🚀