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RUDR TYAGI

@rudratyagi

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I build production AI systems spanning ML, backend, cloud, and MLOps.

India

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What I'm looking for

I'm looking for Applied AI, Machine Learning Engineering, AI Backend, MLOps, or AI infrastructure roles where I can build modular, reproducible, observable, and deployment-ready AI systems for real-world products.

I've built AutoCare, an explainable healthcare AI platform for non-invasive diabetes, heart disease, and stroke risk assessment using 504K+ healthcare records. I led a four-member team, architected three disease-specific ML pipelines, and integrated SHAP explainability, Flask, and MongoDB into an end-to-end application.

At GeoTechnoSoft, I built five production-oriented AI prototypes across RAG, real-time Voice AI, data analytics, and conversational AI. I redesigned a Whisper and Twilio voice workflow into real-time streaming with Twilio Media Streams, WebSockets, and Google ADK, reducing response latency from roughly 5–6 seconds to about 1 second.

I work across the AI application lifecycle: data processing, model development, APIs, retrieval systems, deployment, and system engineering. My projects emphasize modular, reproducible, observable, maintainable, deployment-ready systems.

I'm particularly interested in the engineering layer between AI models and real-world products, including Applied AI, MLOps, cloud-native AI, LLM applications, and AI infrastructure. I also write about AI engineering, explainability, data engineering, and production ML systems.

Experience

Work history, roles, and key accomplishments

ML Engineer & Project Lead

AutoCare

Oct 2025 - May 2026 (7 months)

Led a 4-member team in developing AutoCare, an explainable healthcare AI system for non-invasive risk assessment of diabetes, heart disease, and stroke using 504K+ healthcare records. Architected 3 independent ML pipelines with disease-specific preprocessing, feature engineering, model training, and inference workflows using Python, Scikit-learn, XGBoost, and Random Forest.

Python scikit learn XGBoost Random Forest SHAP Flask MongoDB ML Pipelines Feature Engineering Model Training Inference Explainable AI Healthcare AI Team Leadership Research

ML Engineer & Project Lead — AutoCare

AutoCare (Independent Project)

Oct 2025 - May 2026 (7 months)

Led a 4-member team in developing AutoCare, an explainable healthcare AI system for non-invasive risk assessment of diabetes, heart disease, and stroke using 504K+ healthcare records.

Architected 3 independent ML pipelines with disease-specific preprocessing, feature engineering, model training, and inference workflows using Python, Scikit-learn, XGBoost, and Random Forest.

Achieved F1 = 0.7

AI/ML Intern

GeoTechnoSoft

Aug 2025 - Oct 2025 (2 months)

Built 5 production-oriented AI prototypes in 2 months across RAG, real-time Voice AI, data analytics, and conversational AI, working with Python, FastAPI, LangChain, Gemini, Twilio, Whisper, and Google ADK.

Developed an EV Market Intelligence data analytics workflow integrating 18+ heterogeneous datasets, with ETL, schema alignment, data cleaning, feature engineering, and exploratory analysis s

Python fastAPI Lang Chain Gemini Twilio Whisper Google ADK RAG Voice AI Data Analytics ETL WebSockets ChromaDB REST APIs Git Testing Documentation

AI-ML Virtual Intern

EduSkills Foundation®

Oct 2024 - Dec 2024 (2 months)

Completed a Google-supported AI/ML virtual internship covering Vertex AI, Vertex AI Studio, Vector Search, Prompt Engineering, Multimodal RAG, Responsible AI, and Gemini-based AI applications through guided Google Cloud labs.

Independently built an AI Nutritionist application using Python, Gemini, Streamlit, and Pillow, demonstrating multimodal image understanding for food analysis, calorie est

Vertex AI Vector Search Prompt Engineering Responsible AI Gemini Python Streamlit