class IQROGUEREX:
def __init__(self):
self.role = "AI/ML Systems & Core Deep Learning Engineer"
self.domains = [
"Decoder-Only Transformers",
"Dense Vector RAG & FAISS",
"Quantized Local Inference (GGUF/AWQ)",
"High-Throughput Streaming Engines"
]
self.stack = ["PyTorch", "Hugging Face", "FastAPI", "React", "AWS"]
def operational_status(self) -> str:
return "Converging loss functions, optimizing tokens/sec, and shipping production models."| Focus Area | Core Stack & Architectures | Operational Execution |
|---|---|---|
| 🧠 Deep Learning & Modeling | Decoder-Only Transformers LoRA QLoRA FlashAttention |
Custom pre-training & PEFT pipelines |
| ⚡ Retrieval-Augmented Generation | FAISS ChromaDB Dense Embeddings Cross-Encoders |
Multi-stage hybrid search & re-ranking |
| ⚙️ Local LLMs & Acceleration | Ollama vLLM TensorRT-LLM GGUF AWQ |
Low-bit quantization & low-latency inference |
| 🌐 Production Engineering | FastAPI SSE / WebSockets AWS Serverless Docker |
Async streaming tokens & scale-to-zero microservices |
flowchart LR
%% Style definitions
classDef data fill:#1e1e2e,stroke:#89b4fa,stroke-width:2px,color:#cdd6f4;
classDef process fill:#181825,stroke:#f38ba8,stroke-width:2px,color:#cdd6f4;
classDef engine fill:#11111b,stroke:#a6e3a1,stroke-width:2px,color:#cdd6f4;
classDef output fill:#313244,stroke:#f9e2af,stroke-width:2px,color:#cdd6f4;
subgraph INGESTION ["📥 Ingestion Pipeline"]
A[Documents / Corpus]:::data --> B[Dense Embeddings]:::process
B --> C[(FAISS / ChromaDB)]:::data
end
subgraph INFERENCE ["⚡ Inference Engine"]
Q[User Query]:::data --> R[Context Retrieval]:::process
C -.->|Nearest Neighbors| R
R --> S[Cross-Encoder Reranker]:::process
S --> T[vLLM / Quantized LLM]:::engine
T --> O[Streaming Tokens SSE]:::output
end
|
|
|
“Whatever in creation exists without my knowledge exists without my consent.”