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AJ

ANNEPAKA JAGADISH

AI Engineer · GenAI

Hyderabad, Telangana, India

@annepaka_jagadeesh

LinkedIn GitHub

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B.Tech in Information Technology at Guru Ghasidas Vishwavidyalaya · Graduating 2026

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About

Entry-level AI engineer with a B.Tech in Information Technology, building LLM applications in Python. Has experience building and evaluating RAG systems and an AI coding agent with validation and repair workflows.

Experience

  • AI/ML Intern
    AnantaHQ
    May 2026 – Aug 2026

    Built and tested a RAG-based application using small language models (SLMs), working on document retrieval and response generation. Evaluated the application's responses against expected answers to assess answer quality and retrieval accuracy. Contributed to the architecture of AI Content Studio, a locally hosted content generation tool powered by local language models, keeping user data on-device.

Education

Skills

3 to 6 months 3 to 6 months A few months of practice with it.
RAG Machine Learning
<3 months <3 months Just getting started - under three months. This is also what shows when a level has not been set.
SQL Python FastAPI Pgvector LangChain LangGraph AI Agents TypeScript LlamaIndex Fine-tuning LLM evaluation AI system design Model Context Protocol API Integration Understanding Data Structures and Algorithms

Tools / apps / platforms

<3 months <3 months Just getting started - under three months. This is also what shows when a level has not been set.
Git Vite GitHub QDrant Langfuse PostgreSQL Hugging Face Amazon Web Services AWS

Projects

  • AI Coding Agent: React App Builder with Live Preview ↗
    Python, LangGraph, FastAPI, PostgreSQL, React, Vite, Tailwind CSS

    Agent that turns plain-English requests into a working React app with live preview, routes messages by intent, validates output with TypeScript/ESLint/Vite, and repairs failures with rollback to the last working Git commit.

  • Documentation RAG Assistant for LangChain & LangGraph ↗
    Python, FastAPI, LlamaIndex, LangGraph, Qdrant, PostgreSQL, DeepEval

    Question-answering system over 150 pages of LangChain and LangGraph documentation across 3 library versions, with cited answers and evaluation on a hand-checked dataset. Improved retrieval with BM25 and Reciprocal Rank Fusion in Qdrant and measured faithfulness and answer relevancy.

Courses & certifications

  • Building with the Claude API · Anthropic · 2026

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