AI/ML Engineer passionate about building end-to-end AI systems. I believe in understanding AI by implementing from scratch — from Vision Transformers to multi-agent architectures.
Focus: Computer Vision • Research Implementations • GenAI & Agents • MLOps
Supervision YOLOv5-v8 Object Detection Image Segmentation DeepSORT ByteTrack Optical Flow
LangGraph AG2 (AutoGen) Agno RAG DeepSeek Prompt Engineering OpenAI API
Seaborn Scikit-learn Statistical Analysis Feature Engineering A/B Testing
Streamlit FastAPI Flask Gradio
EC2 S3 Lambda CI/CD MLflow DVC
Pinecone Chroma FAISS Vector Databases
Tennis Vision ★ 23Real-time tennis match analysis system with advanced computer vision
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Multi-sport CV analysis pipeline for football/soccer
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Deep learning medical imaging system for cancer detection
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AI-powered molecular research & drug discovery platform
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Conversational AI with real-time web search capabilities
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Production-ready Deepfake Detection MLOps Pipeline
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Building architectures from scratch to truly understand them.
| Paper | Implementation | Key Details |
|---|---|---|
| Vision Transformer | ViT | 16×16 patch embedding, 12-layer encoder, multi-head attention |
| LoRA & QLoRA | PyTorch-LoRA | Low-rank adaptation, 4-bit quantization, 83% VRAM reduction |
| Vision-Language Models | VLMverse | PaLiGemma, SigLIP, cross-modal attention fusion |
| Project | Description |
|---|---|
| AgentForge ★ 2 | Multi-agent orchestration with CrewAI, LangGraph, PhiData |
| Travel Planner | 4-agent system: Flight, Hotel, Itinerary, Budget |
| Google ADK | Production agent patterns with Google ADK |
Comprehensive implementations from fundamentals to SOTA:
- ML & Deep Learning — Classical ML, CNNs, RNNs, Transformers, NLP
- Computer Vision — RCNN, YOLO, U-Net, Mask R-CNN, tracking
- PyTorch Deep Dive — Autograd, custom layers, DDP, AMP
- RAG Systems — Vector DBs, chunking, retrieval, production patterns
Open to: AI/ML Engineering • Computer Vision • GenAI/LLMOps
"Building AI systems by understanding them from first principles"