- 🚀 AI Engineer focused on LLMs, RAG, and AI Agents
- 🏗️ Build end-to-end AI systems (data → embeddings → APIs → UI)
- ⚡ Strong focus on production-ready, scalable AI solutions
- 🎯 Interested in GenAI, intelligent automation, and real-world impact
- Production-grade RAG pipelines
- LLM-powered applications with memory + retrieval
- Semantic search & vector databases
- AI system design (latency, scalability, modularity)
| Programming Languages | Frameworks & Libraries |
|---|---|
| AI / ML & Data | Databases & Vector Search |
|---|---|
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| Generative AI & RAG | Tools & Systems |
|---|---|
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- Built a production-ready RAG AI agent for financial Q&A
- Designed full pipeline: ingestion → embedding → retrieval → generation
- Indexed 100+ financial documents for semantic search
- ⚡ Achieved <1s latency with context-aware responses
- 🧠 Added memory + metadata filtering (↑35% relevance)
- Built a full-stack AI platform for document intelligence
- Implemented RAG pipeline + semantic search
- Developed APIs (FastAPI) + frontend (React)
- Designed scalable modular architecture
- Enabled AI-driven insights from financial documents
- I don’t just build models — I build complete AI systems
- Focus on latency, retrieval quality, and real-world usability
- Strong understanding of RAG architecture (not just using APIs)
- Experience with production-style backend + AI integration
- 📧 rahulchoudhary5266@gmail.com
- 💼 https://www.linkedin.com/in/rahulchoudhary2610
- 💻 https://github.com/26rahulchoudhary
- Advanced multi-agent systems
- Improving RAG retrieval & reranking
- Scaling AI systems for real-world deployment