Building production-ready AI systems — from model logic to full-stack deployment.
🎓 B.Tech in AI & Data Science
💼 AI Engineering Intern @ BigVision
- Retrieval-Augmented Generation (RAG)
- Agentic AI Architectures
- Multi-Agent Orchestration
- Structured Extraction Pipelines (VLMs)
- LLM Routing & Tool Use
- Defect Detection & Anomaly Modeling
- Object Detection Pipelines (YOLO / ONNX)
- OCR & Document Intelligence
- Vision-Language Model Integrations
- FastAPI AI Backends
- Streamlit & Next.js Interfaces
- Vector Databases (FAISS / Chroma)
- Modular & Scalable Architectures
LangChain • AGno • OpenAI • Groq
FAISS • ChromaDB
Pydantic Structured Outputs
PyTorch • OpenCV • YOLO • ONNX
Vision-Language Models
FastAPI • PostgreSQL • Docker
Streamlit • Next.js
- Multi-document semantic retrieval
- LangChain + Vector DB pipeline
- Structured legal Q&A system
- Streamlit interactive interface
- Domain-based agent routing
- Financial + health retriever separation
- FAISS-backed retrieval system
- Structured LLM responses via Pydantic
- PyTorch-based anomaly classifier
- Autoencoder experimentation
- Production-style data pipeline
- Binary defect / non-defect system
- Designing reliable agentic AI systems
- Building modular RAG architectures
- Improving structured document extraction pipelines
- Developing production-ready AI APIs
- 🔗 LinkedIn — https://www.linkedin.com/in/nareshrajaml
- 🤗 Hugging Face — https://huggingface.co/nareshmlx
- 📧 Email — nareshrajaml@gmail.com
- Systems > Demos
- Modularity > Monoliths
- Measurable Performance > Hype
- Practical AI > Toy Projects