Degavath Anand Nayak profile photo 1 on Tal

Degavath Anand Nayak

Full stack developer @GE HealthCare | AngularJS | ReactJS | Java | Python | C++ | building scalable web applications | Medical Imaging Systems

Bengaluru

6+ years of work experience

GE Healthcare
Jun 20242 yrs 5 mosPresent

On-site

- 🚀 Engineered a camera-based body-contour service achieving 3.4x faster point-cloud generation (89ms → 26ms), eliminating manual repositioning delays for automated patient positioning during MRI/CT scans - Slashed /pointcloud-statistics API response time by 87% (5,000ms to 661ms) by redesigning the backend using the Hexagonal (Ports and Adapters) design pattern. - 🛠️ Fixed a critical firmware landmark defect affecting **4,000+ hospital sites** by building an OpenCV-based peak-detection algorithm on point-cloud elevation data — preventing patient mispositioning during scans - 📈 Scaled imaging platform deployment from 1,000 to 4,000+ hospitals in 12 months, enabling AI-driven positioning technology for 10,000+ radiology professionals - ⚡ Re-architected the imaging pipeline for 76% CPU reduction (100%→24%) and 85% memory reduction (1.4GB→204MB), delivering mission-critical stability for hospital imaging consoles - 🤖 Led development of a GPU-accelerated (NVIDIA) keypoint-detection module using BodyPoseNet, hitting 97% keypoint-detection accuracy; mentored 1 intern as part of GE's autonomous-imaging initiative - 🎓 Mentored an intern and developed an RGB frame correction solution using computer vision enabling direct gantry-mounted camera positioning, eliminating additional mounting costs and installation complexity for customers and field engineers. - ⏱️ Cut CI/CD pipeline runtime from 18 min to 8 min (56% faster) by identifying and removing key bottlenecks
GE Healthcare
Aug 20221 yr 11 mosJun 2024

Bengaluru, Karnataka, India

- 📦 Slashed the kyma-single-wave micro-frontend bundle size 40x (1027KB → 24KB), dramatically improving UI responsiveness on hospital imaging consoles during time-sensitive scans - 🔍 Ran a full FMEA analysis, identifying 10+ critical failure modes, and systematized fixes that delivered production-grade reliability for hospital-deployed systems - 🔗 Integrated GE Protocol Management MFE into the main MRUI platform, unblocking 70+ cross-functional teams and accelerating product velocity across imaging divisions - 🧩 Modernized 13 micro-frontend applications to a standardized EDS architecture, ensuring consistent UX for radiology technicians across MRI, CT, and X-Ray
NRITYAM - The Dance Club of NIT Raipur
Jun 20201 yrMay 2021

Raipur, Chhattisgarh, India

NRITYAM - The Dance Club of NIT Raipur
Jun 20191 yrMay 2020

Raipur, Chhattisgarh, India

Entrepreneurship Cell, NIT Raipur
Sep 20188 mosApr 2019

Raipur, Chhattisgarh, India

LeetCode
@Anand_Nayak

Degavath’s AI Stack

claude
claude
gemini
gemini
openai
openai
claudecode
claudecode
ChatGPT Images
ChatGPT Images
Microsoft Copilot
Microsoft Copilot

Education

National Institute of Technology Raipur

B.Tech - Bachelor of Technology, Computer science and Engineering

National Institute of Technology Raipur

20184 yrs2022

Intermediate, Maths, Physics, Chemistry

TTW IIT Study Centre

20162 yrs2018

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Headline

Full stack developer @GE HealthCare | AngularJS | ReactJS | Java | Python | C++ | building scalable web applications | Medical Imaging Systems

About

- I build performance-critical computer vision and imaging systems that run inside MRI and CT scanners across 4,000+ hospitals worldwide — software where a 60-millisecond delay isn't a UX annoyance, it's a delay in a patient's diagnosis. - Over 4 years at GE HealthCare, I've owned the full stack of that problem: from GPU-accelerated keypoint detection models (97% accuracy) that automate patient positioning, to OpenCV-based algorithms that fixed a firmware issue affecting 4,000+ hospital sites, to rebuilding imaging pipeline architecture for a 76% CPU and 85% memory footprint reduction on hospital consoles that can't afford to crash mid-scan. - What I care about is the intersection of computer vision, systems performance, and reliability at scale — building things that are not just accurate in a demo, but stable, fast, and safe in a hospital running them 24/7 on thousands of machines. > **Selected impact:** > 🔹 3.4x faster point-cloud generation (89ms → 26ms) for automated patient positioning > 🔹 Scaled camera-service deployment from 1,000 → 4,000+ hospitals in 12 months > 🔹 76% CPU reduction, 85% memory reduction on production imaging consoles > 🔹 40x smaller frontend bundle (1027KB → 24KB) shipped across hospital UI consoles

Location

Bengaluru

AI stack

claude

gemini

openai

claudecode

ChatGPT Images

Microsoft Copilot

Work experience

Software Engineer at GE Healthcare, Jun 2024 - Present · 2 yrs 5 mos, On-site, - 🚀 Engineered a camera-based body-contour service achieving 3.4x faster point-cloud generation (89ms → 26ms), eliminating manual repositioning delays for automated patient positioning during MRI/CT scans - Slashed /pointcloud-statistics API response time by 87% (5,000ms to 661ms) by redesigning the backend using the Hexagonal (Ports and Adapters) design pattern. - 🛠️ Fixed a critical firmware landmark defect affecting **4,000+ hospital sites** by building an OpenCV-based peak-detection algorithm on point-cloud elevation data — preventing patient mispositioning during scans - 📈 Scaled imaging platform deployment from 1,000 to 4,000+ hospitals in 12 months, enabling AI-driven positioning technology for 10,000+ radiology professionals - ⚡ Re-architected the imaging pipeline for 76% CPU reduction (100%→24%) and 85% memory reduction (1.4GB→204MB), delivering mission-critical stability for hospital imaging consoles - 🤖 Led development of a GPU-accelerated (NVIDIA) keypoint-detection module using BodyPoseNet, hitting 97% keypoint-detection accuracy; mentored 1 intern as part of GE's autonomous-imaging initiative - 🎓 Mentored an intern and developed an RGB frame correction solution using computer vision enabling direct gantry-mounted camera positioning, eliminating additional mounting costs and installation complexity for customers and field engineers. - ⏱️ Cut CI/CD pipeline runtime from 18 min to 8 min (56% faster) by identifying and removing key bottlenecks

Edison Engineering Developent Program at GE Healthcare, Aug 2022 - Jun 2024 · 1 yr 11 mos, Bengaluru, Karnataka, India, - 📦 Slashed the kyma-single-wave micro-frontend bundle size 40x (1027KB → 24KB), dramatically improving UI responsiveness on hospital imaging consoles during time-sensitive scans - 🔍 Ran a full FMEA analysis, identifying 10+ critical failure modes, and systematized fixes that delivered production-grade reliability for hospital-deployed systems - 🔗 Integrated GE Protocol Management MFE into the main MRUI platform, unblocking 70+ cross-functional teams and accelerating product velocity across imaging divisions - 🧩 Modernized 13 micro-frontend applications to a standardized EDS architecture, ensuring consistent UX for radiology technicians across MRI, CT, and X-Ray

Core-Coordinator at NRITYAM - The Dance Club of NIT Raipur, Jun 2020 - May 2021 · 1 yr, Raipur, Chhattisgarh, India

Coordinator at NRITYAM - The Dance Club of NIT Raipur, Jun 2019 - May 2020 · 1 yr, Raipur, Chhattisgarh, India

Volunteer at Entrepreneurship Cell, NIT Raipur, Sep 2018 - Apr 2019 · 8 mos, Raipur, Chhattisgarh, India

Education

B.Tech - Bachelor of Technology, Computer science and Engineering, National Institute of Technology Raipur

Intermediate, Maths, Physics, Chemistry, TTW IIT Study Centre

Public links

leetcode: https://leetcode.com/Anand_Nayak