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Vetted Talent

Charu Jain

Vetted Talent

Computer vision engineer with 12 years of experience in the IT industry.

1) Languages: Python, Java, C, Scala

2) Machine learning: Statistical Modelling, Bayesian statistics, ML algorithms, Neural networks(Library - Tensorflow 2), LLM, Langchain, RAG

3)Web development-Angular JS, Web services (REST, SOAP)

For details about my projects please refer to the section below.

  • Role

    Senior Module Leader

  • Years of Experience

    11 years

Skillsets

  • object detection
  • rag
  • LLM
  • LangChain
  • Docker
  • Computer Vision
  • Statistical shape modelling
  • TensorFlow
  • Springboot
  • REST
  • OpenMP
  • OpenCV
  • Open3D
  • Deep Learning
  • CUDA
  • Classification
  • Bayesian Statistics
  • AngularJS
  • Scala
  • C++
  • Segmentation
  • Kubernetes
  • Keras
  • Python
  • Java
  • SOAP

Vetted For

10Skills
  • Roles & Skills
  • Results
  • Details
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    Front End Developer (3D Image Processing)AI Screening
  • 46%
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  • Skills assessed :cornerstone.js, DICOM, itk.js, NIFTI, FrontEnd, vtk.js, ES6, JavaScript, Python, React Js
  • Score: 46/100

Professional Summary

11Years
  • Jan, 2024 - Jul, 20251 yr 6 months

    Senior Module Leader

    Motherson Technologies And Services
  • Dec, 2021 - Oct, 20231 yr 10 months

    Senior Computer Vision Engineer

    Vehant Technologies
  • Jan, 2018 - Dec, 2018 11 months

    API Developer

    Barclays
  • Jul, 2011 - Jan, 20175 yr 6 months

    Systems Engineer

    Infosys
  • Jan, 2017 - Dec, 2017 11 months

    Senior Module Lead

    Sopra Steria

Applications & Tools Known

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    Tensorflow

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    RAML

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    REST API

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    SciPy

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    Spring Boot

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    VTK

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    3dslicer

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    Scalismo

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    Computer Vision

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    NumPy

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    Java

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    Python

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    Keras

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    Google Kubernetes Engine

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    BigPanda

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    C++

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    Scala

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    Angular

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    CUDA

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    Azure Machine Learning Studio

Work History

11Years

Senior Module Leader

Motherson Technologies And Services
Jan, 2024 - Jul, 20251 yr 6 months
    Designed solutions and led a team to implement multiple projects including Document Image Comparison (automated visual comparison of engineering drawings using computer vision), Bill of Material Extraction (LLM and RAG for contextual understanding), Crimp Cross-section Analysis (image registration and segmentation using UNet and VAE), and Forklift Safety (YOLO-based tracking system deployed on Jetson Nano). Applications deployed on AWS Sagemaker and tracked with MLFlow.

Senior Computer Vision Engineer

Vehant Technologies
Dec, 2021 - Oct, 20231 yr 10 months
    Conceptualized and prototyped product enhancements for Under Vehicle Scanning System (UVSS), including Bayesian coherent point drift algorithm for point cloud registration, statistical shape modeling of video data, FLANN and KDTree for nearest neighbour search, deep learning for image classification and segmentation (ResNet, UNet), and performance optimization using CUDA, OpenMP, LAPACK, and MAGMA.

API Developer

Barclays
Jan, 2018 - Dec, 2018 11 months
    Developed new microservice for Pingit mobile app using REST framework and Spring Boot. Managed deployment and microservices using Kubernetes.

Senior Module Lead

Sopra Steria
Jan, 2017 - Dec, 2017 11 months
    Developed web application (Online Quotation Tool) using Javascript and J2EE technologies following scrum methodology.

Systems Engineer

Infosys
Jul, 2011 - Jan, 20175 yr 6 months
    Worked on internet banking modernization, new business strategy management tool, and internet banking regression for clients National Australian Bank and American Express.

Major Projects

4Projects

Image Segmentation of Tooth in CBCT Image Using Iterative 3D U-Net Training to Build a Tooth SSM (Master Thesis)

    Automated 3D tooth segmentation from CBCT images using 3D U-Net for semantic segmentation to build statistical shape models. Evaluated model quality based on generalisation, specificity, and compactness.

3D Slicer Extension

    Developed a user-friendly UI extension using the open source 3D Slicer framework for editing image labels in 3D.

Combining Statistical Shape Model of Complete Tooth from CBCT Images with Laser Scan Data of Its Crown (Master Project)

    Created a hybrid 3D model by combining full morphology from CBCT images and details from laser scans using Metropolis Hastings algorithm with iterative closest point proposals.

QA Bot to Read Document (Coursera)

    Built a generative AI application using LangChain and RAG for document question answering.

Education

  • B.Tech, Information Technology

    Jaypee University Of Information Technology (2011)
  • M.Sc, Computer Science

    University Of Basel (2021)

Certifications

  • MuleSoft Certi Developer - API Design Associate (RAML 1.0) (cid:221) Oct 2018 - Oct 2020

AI-interview Questions & Answers

I have nine years of experience. My first six years were in software engineering, where I worked on front-end web development and deployment activities. After that, I completed my master's for two and a half years and specialized in machine learning. One and a half years of my master's program involved working on a project related to medical images, which included DICOM files and the creation of tools for image segmentation. After that, I worked with back-end technologies, where I worked on point cloud image processing.

Well, I have built an app for a web application that allows editing 3D segmentations using 3D Slicer software. 3D Slicer has an API that I used to build web components which could access the machine learning algorithm and predict the segmentation on the web app. We were also able to edit using the APIs, applying web components that could edit the images on the screen. I was able to do this using the 3D Slicer API.

In what ways would ES6 and ReactJS aid me in building web apps? Well, ReactJS will provide web components which would help me in building the web application. ES6 provides a standardized way of doing the same. So using the web components provided by ReactJS, I will be able to provide interactivity with the application.

Cornerstone.js is a JavaScript library that helps interact with medical images. It facilitates working with medical images, and it helps in visualizing 3D images in different directions and formats. A framework like Cornerstone.js, in combination with React.js, can be used for loading DICOM files or NIFTY files and visualizing them in 3D space, including XYZ directions, the XY plane, the YZ plane, the ZY plane, or as a 3D rendering format.

The project structure. Well, the project structure that I use is that I keep the constant variables in a separate file. And I organize my project in such a way that it is that the components are reusable and they are upgradable. And all the components are also unit tested as well as system tested end to end so as to build a scalable platform for a front-end application.

Can you provide an example of a complex front-end feature you developed in a technology stack that you used? Well, this was a long time back when I was working in web application development for a banking application. We were able to build a login feature with normal web components for login and transition between two different applications, where you could roll down and dynamically load more content on the screen.

Consider the below Python function that loads DICOM image and visualizes 3D segmentation using what potential issues can you spot and how can you suggest to improve? So it is able to read the file and return the pixel array. Well, there should be a check whether the data which is loaded is present or not. So there needs to be an exception which needs to be added. So that there is a fallback in case the file is not present. There needs to be an exception rule in load image while it's loading the file. That's all.

Review the following charges against a student that uses consumption as a premedical measure and whether you might be wrong with it. Load image should be present before enabling the element Thank you for watching!

In the current project at Vihant where I was working, we had to alter the image displayed in 2D to 3D, and it also had to highlight the 3D points on the screen where a foreign object was present. Working with 3D data using a new library was a difficult task, so the existing 2D image loading had to be changed to 3D image loading, as well as rendering that on a 3D view box, which was a challenging situation.

What are the strategies you have employed to ensure a small functional GPU-dependent browser? Well, GPU-dependent browser application, I have used for 3D rendering of images or 3D reconstructed images that I was working with. So, we used libraries that also provided functions for working on the GPU. So, the idea was that if a GPU is available, then it works on the GPU; else, there is a fallback to the CPU in case GPU capability is not present. To ensure the smooth functioning of the GPU-dependent browser application, we had to make it independent of the hardware being used. So, the idea was that we were using open-source libraries so that they are not dependent on vendor-specific hardware configurations. So, we were using open-source libraries for GPU-based applications.