---
title: "Rudra Tyagi - ML Engineer & Project Lead — AutoCare in Noida | Tal"
description: "Meet Rudra Tyagi, ML Engineer | Applied AI & MLOps | FastAPI · AWS · XGBoost · RAG based in Noida."
image: "https://tal.club/meet/rudra-tyagi-1/share-image"
---

# Rudra Tyagi on Tal

## Headline

ML Engineer \| Applied AI & MLOps \| FastAPI · AWS · XGBoost · RAG

## About

I build deployment\-oriented machine learning systems focused on APIs, inference workflows, analytics platforms, and cloud\-native AI applications\. Currently leading AutoCare — an explainable clinical AI platform for Heart Disease, Stroke, and Diabetes risk prediction built on 504K\+ healthcare records using XGBoost, SHAP, FastAPI, Docker, and AWS\. Previously worked as an AI/ML Intern at GeoTechnoSoft, where I contributed to RAG applications, voice AI systems, ETL analytics platforms, and real\-time AI workflows using FastAPI, LangChain, Whisper, WebSockets, and cloud tooling\. My primary interests include: • ML Systems Engineering • MLOps & Deployment Workflows • FastAPI & AI Backend Systems • Explainable AI • Cloud\-Based ML Infrastructure • Applied AI Engineering Currently preparing for MS in AI/ML Systems \(Fall 2027\) while continuing to build production\-oriented AI systems and strengthen ML infrastructure expertise\. Open to: • ML Engineering Internships • Research Collaborations • AI/ML Engineering Discussions • Global Remote Opportunities

## Location

Noida

## Work experience

ML Engineer & Project Lead — AutoCare at AutoCare \(Independent Project\), Oct 2025 \- May 2026 · 8 mos, Meerut, Leading a team of 4 to build AutoCare — a non\-invasive, lifestyle\-based Early Risk Screening system for cardiometabolic diseases \(Heart Disease, Diabetes, Stroke\)\. \- Architected 3 independent ML pipelines on a shared ingestion → validation → transformation → training framework; trained on 192K\+ heart, 73K\+ diabetes, and 239K\+ stroke records\. \- Heart disease pipeline \(Random Forest, F1=0\.713, Recall≈1\.0\) demonstrated strong lifestyle\-feature correlation; diabetes and stroke pipelines established baseline performance with identified limitations from class imbalance and absent clinical biomarkers — documented transparently in published proceedings\. \- Integrated full SHAP explainability layer \(TreeExplainer \+ LinearExplainer\) generating per\-patient feature contribution scores and top\-5 risk factor reports\. \- Deployed : AWS cloud \- Research presented at ICET 26 \(BVIMIT Navi Mumbai, April 2026\); proceedings indexed on Zenodo — DOI: 10\.5281/zenodo\.19864529\. Two papers produced: one comprehensive review, one original empirical study\. Stack: Python · XGBoost · SHAP · Random Forest · Flask · Streamlit · AWS · Docker · Pandas · scikit\-learn

AI/ML Intern at GeoTechnoSoft, Aug 2025 \- Oct 2025 · 3 mos, Noida, • Delivered 5 production\-ready ML/NLP prototypes in 3 months — RAG chatbots, real\-time voice assistants, and BI dashboards — all handed over with documentation and modular codebases for team continuity\. • Built and deployed an EV Market Analysis platform processing 18\+ CSV/Excel datasets through a full ETL pipeline; answered 25\+ business intelligence questions via interactive Streamlit dashboards used by non\-technical stakeholders\. • Architected a bidirectional voice assistant integrating Whisper \(STT\), FastAPI, and Twilio SDK — processed live caller audio with sub\-second transcription latency and real\-time TTS response streaming\. • Engineered a LangChain RAG chatbot with document ingestion, vectorization, and context\-aware query answering; implemented session state management and failover strategies for production\-grade uptime\. • Maintained production standards across all 5 deliverables: modular architecture, \.env\-based environment separation, Git branching, and full handover documentation — ensuring team continuity post\-internship\.

AI\-ML Virtual Intern at EduSkills Foundation®, Oct 2024 \- Dec 2024 · 3 mos, India, Completed a structured AI\-ML virtual internship program under Google for Developers, AICTE, and EduSkills Foundation — one of 10,000\+ selected participants nationally\. \- Completed hands\-on labs covering Generative AI, Google Cloud & Vertex AI, Vector Search, Multimodal RAG, and introductory MLOps for GenAI\. \- Gained practical exposure to prompt engineering, embedding\-based retrieval, and responsible AI frameworks on Google Cloud infrastructure\. \- Applied Vertex AI tools for model deployment and cloud\-based AI workflows through guided lab exercises\.

## Education

Bachelor of Technology, Computer Science, Meerut Institute of Engineering and Technology\(MIET\)

[View the original profile](https://tal.club/meet/rudra-tyagi-1)

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