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Machine Learning Engineer Resume Example

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MACHINE LEARNING ENGINEER
Links
  • https://www.linkedin.com/in/anuradha-chandrashekar-a2092040/
Professional Summary

Machine Learning engineer with an established track record of research and building successful customer applications for over 5 years. Background includes machine vision, image and video processing, algorithm development, data collection and analysis. Experience creating machine learning models and retraining systems and transforming data science prototypes to production-grade solutions. Consistently employs statistical methods and designs to yield real gains from model changes.

Skills
  • Programming Languages: Python, C, C++, MATLAB
  • Machine Learning Frameworks: Pytorch, Tensorflow,Keras
  • ML tools and libraries: CUDA, OpenCV, Scikit-learn, NumPy, QGIS, Jupyter, Matplotlib, Seaborn.
  • Configuration Management: Git, SVN
  • Applications: Microsoft Office, Jira, Confluence.
  • Ability to understand and implement machine learning research papers.
  • Excellent communication and organization skills.
  • Proven history handling various projects at a time.
  • Strong problem solving and debugging/troubleshooting skills.
  • Software Improvements
Work History
04/2018 to Current
Machine Learning EngineerCompany Name – City, State

Camera ISP-BOX service:

  • Assist in ISP-BOX bring up which involves offline processing of images through ISP using MIPI tx.
  • Flash camera ISP with appropriate firmware and processing images from different cameras of different revisions efficiently.
  • Developed service and ingestion pipeline using Kubeflow, S3 and SQS capable of handling a volume of 2.4million images throughout data collection season.
  • Designed and developed ROM release process which is widely used across team.

QR Code Calibration Model:

  • Trained QR code model with SOTA arhitecture.
  • Deployed model by converting it to real time TensorRT engine, which is optimized to run on NVIDIA Xavier embedded platform.

Image Processing Pipeline - Pre-release Camera

  • Bring up pre-release camera and develop image processing pipeline to support HDR processing including debayer, white balance, tone-mapping, color correction, flat field correction etc.
  • Collaborated with different teams and outside vendors to customize camera for intended application.

Plant Phenotyping using hyperspectral imagery

  • Software development for drone-based images using state of the art deep learning techniques to assess plant health, traits and other characteristics.

Accurate plant stand count software

  • Developed stand count model using Tensorflow to accurately detect and count number of plants in a plot captured from drone. Software module can accurately detect plants as small as 10-pixel size.

Nitrogen deficiency

  • Digitize field data using QGIS and Python into a format which can be easily ingested by software.
  • Developed regression model to correlate SPAD and biomass to predict plant yield and evaluate nitrogen deficiency in plants based on yield.
  • Images are captured from drone at 20m altitude using RGB, NIR cameras and Lidar.
  • Development is done using Python and Tensorflow.
02/2017 to 08/2017
Research ScientistCompany Name – City, State
  • Literature survey for SOTA models for object detection.
  • Developed model training and evaluation pipeline for vehicle axle detection and counting to automate process of highway electronic toll collection.
  • Developed python module to do model inference and integrate with larger modules.
  • Participated in presentations and demonstrations.
05/2013 to 05/2015
Software Developer - Computer Vision Company Name – City, State
  • Develop computer vision based prototype for Automatic raindrop detection and removal for side view cameras in cars using MATLAB. Convert MATLAB prototype to C to port to embedded device.
  • Selection of prototype camera and develop image processing algorithm using MATLAB for processing RAW images from camera for Automatic raindrop detection and removal application.
  • Develop American Sign Language Gesture Recognition application using Artificial Neural Network (ANN). Developed Graphical User Interface using MATLAB for demonstration.
  • Develop Image Processing software application for automatic image enhancement of 16-bit X-Ray images using C++ and OpenCV. Integrate with larger modules.
  • Develop Computer vision based object detection and distance estimation algorithm for indoor AGV(Automated guided Vehicle) using MATLAB.
Education
12/2017
Master of Science: Electrical And Microelectronic Engineering
Rochester Institute Of Technology - City, State
  • Majored in Machine Learning and Computer Vision
  • Graduated with 3.87 GPA
  • Coursework in Computer VIsion, Deep Learning, Machine Learning, Image and Video Compression, Digital Image Processing and Digital Signal Processing.
UDACITY - Deep Learning NanoDegree
Accomplishments
  • Efficiently handled a team of 8 for a quarter as a part of Team of Teams model.
  • Best Team Performance - Larsen & Toubro Technology Services.
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Resume Overview

School Attended

  • Rochester Institute Of Technology
  • UDACITY - Deep Learning NanoDegree

Job Titles Held:

  • Machine Learning Engineer
  • Research Scientist
  • Software Developer - Computer Vision

Degrees

  • Master of Science : Electrical And Microelectronic Engineering

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