AI Engineer | Turning LLMs into working products | LangChain · RAG · LCEL Agents · Python
Bachelor of Technology, Artificial Intelligence
Anurag university
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I build LLM-powered applications that actually work — not just demos that impress in a notebook. Over the past few months I've gone deep on the AI engineering stack: LangChain pipelines, RAG systems with semantic retrieval, Pydantic-validated structured outputs, and LCEL-based agent workflows. Every project I build ships as a working application, not just code sitting in a repo. Here's what I've built so far: A RAG document assistant with HuggingFace embeddings, ChromaDB, and MMR retrieval A multi-tool agentic city assistant that routes between weather and news APIs autonomously AI-powered generators for emails, cover letters, and task roadmaps — all with structured LLM outputs via Pydantic Right now I'm building a multi-agent research assistant using LCEL — exploring how to coordinate agents across structured reasoning loops without a heavyweight orchestration framework. I'm looking for a remote AI Engineer internship where I can contribute to real LLM application development, pick up production patterns fast, and work alongside engineers who take AI seriously.
Greater Hyderabad Area
Bachelor of Technology, Artificial Intelligence, Anurag university