Abhishek Chandragiri

About Abhishek Chandragiri

I build - for complex, regulated environments — where correctness, reliability, security, and explainability matter far more than demos or benchmarks.My work sits at the intersection of /, with deep hands-on experience designing systems that process real-world medical documents end to end. This includes OCR, layout detection, page classification, field extraction, and decision support built using.Beyond models, I have strong systems and infrastructure expertise. I’ve worked directly with, handling Linux-based GPU bring-up (drivers, CUDA Toolkit, NVCC) and integrating inference runtimes such as Torch, ONNX Runtime, and llama.cpp. I’m comfortable deploying AI services across, using RPM\'s, containers, and Kubernetes depending on the constraints.My work also involves designing AI platforms that operate on, including, within closed, controlled service environments. This has shaped how I approach system design end to end — from validation-heavy pipelines to secure service communication, restricted data flows, and audit-friendly architectures.A core part of my approach is close collaboration with clinical practitioners and clinical reviewers to understand how decisions are made in practice. Through this, I’ve developed strong domain knowledge across, ensuring AI outputs align with real evaluation criteria and are easy to verify and trust.I design AI platforms with, combining ML, rules, validation logic, guardrails, and human review to ensure outputs are dependable in practice. I also focus heavily on, building observability pipelines.I’m most interested in — systems that handle messy data, operate under real constraints, and deliver measurable value. If you’re building serious AI platforms, document intelligence systems, or large-scale ML infrastructure, I’m always open to exchanging ideas.

Experience

1. Data Science and Machine Learning Evangelist

   Medium

   Oct 2023 — Present

   Authored a series of in-depth articles on Medium, focusing on end-to-end machine learning, data science, and AI projects, reaching a wide audience of professionals and enthusiasts. Explained complex technical concepts in an accessible manner, covering topics such as data preprocessing, model development, deployment strategies, and real-world applications. Created detailed tutorials and case studies that demonstrate practical applications of machine learning and AI, helping readers to implement solutions in their own work.

Education

• University of Houston

  Master's degree, Data Science

  2022 — 2023

• DataCamp

  Data Science and Analytics

• Teegala Krishna Reddy Engineering College

  Bachelor's degree, Computer Engineering