class AIEngineer:
def __init__(self):
self.name = "Raj Raavi"
self.title = "AI Engineer"
self.location = "Earth 🌍"
self.languages = ["Python", "SQL", "Bash", "TypeScript"]
self.expertise = {
"LLMs" : ["GPT-4o", "Claude 3.5", "Gemini", "LLaMA", "Mistral"],
"RAG" : ["LangChain", "LlamaIndex", "Pinecone", "Weaviate", "ChromaDB"],
"MLOps" : ["MLflow", "Weights & Biases", "DVC", "Airflow", "Kubeflow"],
"Vision & NLP": ["Hugging Face", "YOLO", "OpenCV", "spaCy", "NLTK"],
"Cloud" : ["AWS SageMaker", "GCP Vertex AI", "Azure ML"],
}
self.currently = "Building next-gen RAG pipelines 🔥"
self.learning = "Agentic AI & Multi-agent systems 🤖"
self.fun_fact = "I think in embeddings and dream in tokens 😄"
def say_hi(self):
print("Thanks for stopping by! Let's build something incredible 🚀")
me = AIEngineer()
me.say_hi()| 🤖 LLMs & Prompt Engineering | 🔍 RAG & Vector DBs |
|---|---|
| Fine-tuning, RLHF, prompt chaining, agents | Semantic search, embeddings, hybrid retrieval |
| 🚀 MLOps & Pipelines | 👁️ Computer Vision & NLP |
| CI/CD for ML, model serving, monitoring | Object detection, NER, text classification |
╔══════════════════════════════════════════════════════════════╗
║ 🤖 Agentic AI & Multi-Agent Orchestration (LangGraph) ║
║ 🧬 Fine-tuning LLMs with RLHF & DPO ║
║ 🔍 Advanced RAG: HyDE, Fusion, Reranking ║
║ ⚡ Real-time ML inference optimization ║
║ 🌐 AI-native app architectures ║
╚══════════════════════════════════════════════════════════════╝
"The best way to predict the future is to build it — with AI."