An up-to-date version of my resume

1. M. Deepak Kumar
Education
Christ University Faculty of Engineering Bangalore, KA
B.Tech. in Electrical & Electronics Engineering, CGPA = 3.47/4.0 June 2014 - April 2018
Experience
Deepak KumarGithub/dPacc·  (+971) 561421678   themdk01@gmail.com
Machine Learning Engineer/Researcher
Machine Learning Engineer Bangalore, KA
Betterhalf.ai June 2018 - Jan 2019
Bangalore, KA
TWB Inc. July 2017 - March 2018
• Contributed in developing the website for the online store.
• Collaborated in design and marketing, right from choosing the products to creating psychologically
enticing ads.
• Was actively involved in marketing the products using Facebook Advertising for detailed
audience targeting.
Co-Founder
• Implemented state-of-the-art papers like “Identification of Promising Couples using Machine
Learning”, “Metric Learning to Rank” and “HCMST” which shows the features important for
happiness in relationships.
• Performed NLP on the about-me section of the users to predict their personality using the
IBM Watson Personality API and Apply Magic Sauce API.
Bangalore, KA
Jan 2019 - June 2019
• Built and deployed a Machine Learning model in production using Tensorflow for Coronary Heart
Disease prediction on Google Cloud Platform and served it as a REST API.
• Developed a model by training a CNN on the NIH-Chest-X-Ray data for various Lung Disease
classification and served it as a REST API using Docker for containerization and Kubernetes for Orchestration.
• Set up an on-premise Kubernetes Infrastructure using Minikube and deployed several Dockerized Deep
Learning Microservices.
• Worked on NVIDIA CLARA platform to build and deploy medical imaging applications into
hybrid (embedded, on-premise, or cloud) computing environments to create intelligent instruments
and automated healthcare.
• Contributed in testing and reporting the alpha version of FHIR API with the Google Cloud Healthcare Team.
Machine Learning Engineer
Spline.ai
• Obtained and processed 250,000+ users data about themselves along with their partner
preference and provided the team with valuable insights, which were implemented as new
features by the engineering team. The implemented features influenced users in changing
their preferences.
• Employed Machine Learning for predicting the users personality, implemented a Polynomial Regression
model with an accuracy of 98.6%.
Abu Dhabi, UAE
June 2019 - Present
• Built and deployed a Machine Learning model in production for No-Show prediction with 70% recall, which
resulted in the reduction of the groups overall No-Show percentage by 2%, improving the slot utilization.
• Led the Machine Learning team in developing potential use cases with high impact through research and
collecting data by collaborating with different teams within the organization.
• Built a Deep Learning based OCR using CRAFT and MORAN models for image detection and recognition in
automating several processes within the organization.
• Productionalized several Machine Learning models and served it as a REST API, which was handling
100+ requests per second using Flask, NGINX, Gunicorn and Gevent on the backend.
• Collaborated with Enlitic Inc., a San Francisco based A.I. startup in developing and streamlining Radiological
workflow and in building deep-learning models for X-Rays.
Machine Learning Engineer
NMC Healthcare

2. Open Source
Relevant Coursework
• Machine Learning
• Deep Learning Specialization
• CNN for Visual Recognition
• RNN & LSTM for Time-Series analysis
• Computer Vision using OpenCV
• Image Processing
• Digital Signal Processing
• Algorithms & Data Structures
• Object Oriented Programming
Machine Learning
Built Machine Learning models involving Regression, Classification, Clustering, Association-Rule
Learning and Ensemble methods to solve business problems. Also used Dimensionality-Reduction
techniques like PCA and LDA.
[Link to Github]
Deep Learning
Built Deep Learning models using ANN for Regression and Classification, CNN for Image Recogni-
tion, RNN and LSTM for Time-Series analysis using Keras and Tensorflow frameworks.
[Link to Github]
Awards & Achievements
• Stood 2nd in the graduating Electrical and Electronics Engineering batch.
• Won the 3rd place for my project on building a Paper-Plate Speaker during Engineer’s day.
• Featured in the Department Merit List in the 7th semester for excellent academic performance.
• Was awarded 1st place for the best Guitarist in Western Electric.
Computer Vision
Basic Image processing techniques using OpenCV. Object detection and segmentation
using SSD, Mask-RCNN and Yolo V2
[Link to Github]
MLaaS
Machine Learning as a Service, built end-to-end machine-learning applications, right from building
the model, to serving them on the web using Bootstrap on the frontend and Flask server on the
backend.
[Link to Github]
Projects
Reflex ML
ReflexML automates your Machine-Learning, right from performing your EDA to Model Building. It
let’s you concentrate on your data and do the heavy-lifiting of performing Machine Learning for you.
Developed the EDA and Model building pipeline with a Flask server on the backend.
Ongoing
[Link to Github]
Koala
A data analysis and manipulation library.
[Link]
Software Engineer Intern Bangalore, KA
Omnicron Tech Pvt. Ltd. April 2016 - Sept. 2016
• Worked on their SaaS product called iBuzzinga.
• Proposed changes for improving the UI of the product.
• Assisted pitching the product to potential customers at a trade show.
• Assisted in developing algorithms to predict what ads would pop up when using the product.
Bangalore, KA
Nov 2016 - Jan. 2017
• Was indulged in web-development. Created and deployed multiple web-applications for clients.
• Was responsible for designing, coding and modifying websites according to client’s specifications.
• Created visually appealing and user-friendly websites using HTML, CSS and Javascript.
Freelance Web Developer
Tab ‘N Space
A tech blog where I share about my experience in dealing with real world software systems.
[Link]

3. Skills
Languages: Python, Javascript.
Operating System: Linux, Windows.
Artificial Intelligence: Machine Learning, Deep Learning, Computer Vision.
Web: Flask, NGINX, Gevent, WSGI, NodeJS, HTML5, CSS3, Bootstrap, jQuery.
Dev-Ops: Docker, Kubernetes, Minikube.
Cloud: AWS, GCP.
Frameworks/Libraries: TensorFlow, Keras, Matlab, OpenCV, Sci-Kit Learn, Numpy, Pandas, Matplotlib, PyTorch,
Tableau.
Publication
“Fabrication of Delta Frame 3D Printer to Reduce Print Time,” In proceedings of National Conference on Mechanical
Engineering and Emerging Technologies(NCMEET), Bangalore, India (23-24) Feb. 2018
Under Prof. Manikandan P., CUFE [Link]