---
title: "T.V.R Charan - AI/ML intern in Hyderabad | Tal"
description: "Meet T.V.R Charan, Computer Science Graduate|Aspiring AI Engineer | Machine Learning • Deep Learning • Computer Vision • Generative AI • Python based in Hyderabad."
image: "https://tal.club/meet/tvr/share-image"
---

# T\.V\.R Charan on Tal

## Headline

Computer Science Graduate\|Aspiring AI Engineer \| Machine Learning • Deep Learning • Computer Vision • Generative AI • Python

## About

Passionate AI/ML Engineer with a strong interest in building intelligent systems that solve real\-world problems through Computer Vision, Deep Learning, and Large Language Models \(LLMs\)\. I enjoy transforming complex data into actionable insights and developing scalable AI solutions from real\-time object detection and clinical information extraction to semantic reasoning engines and automated validation workflows\. My experience spans designing end\-to\-end AI architectures, optimizing machine learning models, and integrating production\-ready backend systems\. Tech Stack • Programming: Python, SQL • AI/ML: Machine Learning, Deep Learning, TensorFlow, CNNs, Prompt Engineering • Computer Vision: YOLOv8, OpenCV, EfficientNet, MediaPipe, OCR, Object Detection, Facial Recognition • LLM & Generative AI: LangChain, LangGraph, Structured Extraction Pipelines, Semantic Reasoning, Multimodal LLM APIs • Backend: FastAPI, REST APIs, Pydantic, Async Processing, Concurrent Pipelines • Databases & Analytics: MySQL, CubeJS • Tools: Git, PyVis I bring a combination of strong technical expertise, problem\-solving skills, and a product\-focused mindset to teams building next\-generation AI solutions\. I thrive in collaborative environments where innovation, learning, and impact go hand in hand\. I'm always open to connecting with recruiters, engineers, and AI enthusiasts\. Feel free to reach out at tvrch\.dm15@gmail\.com\.

## Location

Hyderabad

## Work experience

AI/ML intern at Fire AI, Feb 2026 \- May 2026 · 4 mos, Mumbai, Worked on AI\-driven analytics and semantic reasoning systems focused on automating database understanding, financial data validation, and intelligent information extraction\. Built semantic modeling engines for MySQL, PostgreSQL, and ClickHouse that automated fact detection, relationship inference, bridge table analysis, and CubeJS semantic layer generation using Python and hybrid deterministic \+ AI\-assisted reasoning\. Contributed to financial AI quality assurance by validating extracted data from MIS sheets, PDFs, and CSVs belonging to hundreds of startups under Venture Catalyst\. Collaborated with developers and QA teams to identify extraction issues, validate outputs, and improve pipeline reliability\. Developed an AI\-powered QC verification agent using Gemini 2\.5 Flash that automated re\-evaluation of manual QC decisions through structured JSON validation schemas, reason\-code\-driven prompt routing, and async FastAPI integration\. Successfully integrated the system into production workflows, reducing manual effort while maintaining high accuracy and consistency\.

Software Developer at Providence, Nov 2025 \- Dec 2025 · 2 mos, Hyderabad, Built an AI\-powered Clinical Patient Data Extractor to transform unstructured hospital notes into structured, traceable, and visualized medical knowledge graphs\. Initially replaced a failing vanilla RAG pipeline with a custom LLM\-based extraction system using GPT\-4o/GPT\-5\-mini, achieving significantly lower hallucination and higher recall\. Designed a hybrid extraction schema using Subject → Predicate → Object triplets aligned with medical ontologies, storing outputs as structured JSON/JSONL facts\. Integrated LangGraph for orchestration, normalization pipelines for predicate and timeline consistency, and PyVis for interactive patient graph visualization\. Scaled the system to handle real\-world hospital records ranging from 18k to 240k tokens by implementing chunk\-based extraction and multi\-stage decomposition prompting\. This approach enabled near\-complete hallucination elimination while maintaining extremely high recall and zero data loss across large patient records\. Added evidence\-based traceability and metadata validation, ensuring every extracted fact was linked to a verifiable source snippet from the original notes\. Introduced deduplication mechanisms using hash\-based fact IDs for reliable normalization and quality assurance\. Final solution was presented to stakeholders including the Director of Product Management at Providence Global Centre and clinical experts from Providence Hospitals \(USA\)\. The pipeline was approved, cloned into internal repositories, and considered for future production integration\.

## Education

High School Diploma, Delhi Public School \- India

Bachelor of Engineering, Chaitanya Bharathi institute of technology

[View the original profile](https://tal.club/meet/tvr)

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