Naman Nayak

Building agentic AI systems with LangChain and LangGraph, composing stateful multi-step graphs that plan, call tools, and recover from failure rather than running single-shot prompts.

Developing deep research agents that decompose open-ended queries into sub-tasks, gather evidence across sources, and synthesize cited answers.

Designing RAG pipelines end to end — chunking, embedding, and retrieval over vector databases — with reranking and evaluation to keep answers grounded.

Integrating MCP servers to give agents typed, reusable access to internal tools and data sources.

Applying LLMs to SEO workflows, generating and optimizing structured content and metadata at scale.