class Hill_Patel(AI_Architect):
"""
[INFO] Architecting bridge between Research and Production.
[WARN] High compute requirements detected.
"""
def __init__(self):
self.code = "STiFLeR7"
self.specs = {
"role": "AI Engineer & Full-Stack Architect",
"focus": ["LLMs", "RAG Systems", "Edge AI", "Quantization"],
"driver": "Deploying Scalable Intelligence"
}
def execute_mission(self):
while True:
self.research()
self.optimize()
self.deploy("Production")
LLMs & Retrieval
RAG,Hybrid/Vector Search,Re-ranking,LangChain/LangGraph,Agentic AI
Model Optimization
Quantization (1โ8 bit),Pruning,Distillation,ONNX Runtime,TensorRT,CUDA tuning
ML Engineering
CNNs,Transformers,Multimodal Models,Evaluation (AUROC, F1, hit@K, latency, cost)
Backend & MLOps
FastAPI,Docker,CI/CD (GitHub Actions),Redis,Observability (OTel, Prometheus),GCP,AWS,SQL
Languages & Tools
Python,PyTorch,TensorFlow,NumPy,Pandas,OpenCV,Gradio,Git,Linux
- Published Research: Transforming Urban Solutions for Smart Cities through Crowdsourced Feedback (Mar 2025)
- MCP Mastery: Model Context Protocol - Fractal Analytics
- Professional Certificate: RAG and Agentic AI - Coursera
- Course Completion: Introduction to Neural Networks with PyTorch - Coursera
| PROJECT ID | MISSION BRIEF | CORE TECH |
|---|---|---|
| โก DevPulseAIv2 | [DEV-TOOL] Advanced AI assistant for developer productivity and workflow optimization. |
AI Agents Python LLM |
| ๐ฆ imgshape | [CLI-TOOL] Intelligent dataset analysis framework. Auto-generates reports & pipelines. |
Python PyPI Analysis |
| ๐ฑ Qwen3-iOS | [MOBILE-AI] On-device inference of Qwen3 models optimized for iOS architecture. |
Swift CoreML Quantization |
| ๐ฆพ SpecCraft-AI | [PLATFORM] Analyst-Grade AI Spec Generation Platform with specialized UI. |
Next.js AI-Agents RAG |
| ๐ญ TTGv1-Docker | [ENTERPRISE] Scalable scheduling engine solving complex constraint problems. |
Docker OR-Tools Redis |
| ๐ FastFare-v1-GCP | [SaaS] AI-Logistics assistant. Automated RAG pipeline with vector search. |
GCP RAG FastAPI |
- Most RAG Systems Fail QuietlyโโโHereโs How I Built a 98%-Accurate Agent on a 6GB GPU
- MedMNIST-EdgeAI: Compressing Medical Imaging Models for Efficient Edge Deployment
- LCM vs. LLM + RAG
- Edge-LLM: Running Qwen2.5โ3B on the Edge with Quantization