AI Engineer passionate about pushing the boundaries of what's possible with Generative AI and Natural Language Processing. My work focuses on developing innovative solutions that leverage the latest advancements in transformer architectures, large language models, and multimodal systems.
- π± Iβm currently learning Web Scraping, GenAI, LLMs, Agentics AI & Graphs
def introduce_myself():
me = {
"name": "Your Name",
"role": "AI Engineer",
"specialties": ["Generative AI", "NLP", "LLMs", "MLOps"],
"languages": ["Python", "PyTorch", "TensorFlow", "JAX"],
"current_research": "Efficient fine-tuning techniques for domain-specific LLMs"
}
return me
Languages
A production-ready Retrieval-Augmented Generation system using vector databases and LLMs to provide accurate, context-aware responses.
graph LR
A[Document Corpus] --> B[Chunking]
B --> C[Embedding Generation]
C --> D[Vector Database]
E[User Query] --> F[Query Embedding]
F --> G[Vector Search]
D --> G
G --> H[Context Assembly]
H --> I[LLM Prompt]
I --> J[Response Generation]
A framework for efficient fine-tuning of LLMs using techniques like LoRA, QLoRA, and parameter-efficient training methods.
Comprehensive evaluation suite for benchmarking LLMs across various dimensions including factuality, harmlessness, helpfulness, and domain-specific capabilities.
